SlideShare une entreprise Scribd logo
1  sur  73
Télécharger pour lire hors ligne
Pattern Mining: Getting the
most out of your log data.
Krishna Sridhar
Staff Data Scientist, Dato Inc.
krishna_srd
• Background
- Machine Learning (ML) Research.
- Ph.D Numerical Optimization @Wisconsin
• Now
- Build ML tools for data-scientists & developers @Dato.
- Help deploy ML algorithms.
@krishna_srd, @DatoInc
About Me!
45+$and$growing$fast!
About Us!
+ =
Questions?
• (Now) We are monitoring the chat window.
• (Later) Email me srikris@dato.com.
Webinars
About you?
Creating a model pipeline
Ingest Transform Model Deploy
Unstructured Data
exploration
data
modeling
Data Science Workflow
Ingest Transform Model Deploy
GraphLab(Create(
Train
Model
Pipeline
Deploy
Models
Serve
Requests
(REST API)
Monitor
Services
Get Live
Feedback
Update
Pipelines
Prototype &
Develop
Model
Pipelines
Update Live
Experiment
Deploy New Pipeline
Dato(Predic2ve(Services(
Dato’s
Products Dato(Distributed(
We can help!
Log Journey
Lots of data
Insights Profits
Log Mining: Pattern Mining
Logs are everywhere!
Machine Learning in Logs
Source: Mining Your Logs - Gaining Insight Through Visualization
Coffee shop
Coffee Shops Menu
Receipts
Coffee Shops Menu
Coffee Store Logs
Frequent Pattern Mining
What sets of items were bought together?
Real Applications
Real Applications
Real Applications
Log Mining: Rule Mining
Can we recommend items?
Rule Mining
Real Applications
Log Mining: Feature Extraction
Feature Extraction
0 1 0 0 0 0 1 1 0
1 1 0 0 1 0 0 0 0
0 0 1 1 1 0
Receipt Space Features in
Menu Space
ML
3 Useful Data Mining Tasks
Rule MiningPattern Mining Feature Extraction
Demo
ML is not a black-box.
Transparency
Learning is also about understanding.
Interpretability
Whatever can go wrong, will go wrong.
Diagnosis
Moving on
Pattern Mining Explained
Formulating Pattern Mining
N distinct items → 2N itemsets
Formulating Pattern Mining
Find the top K most frequent sets of length at least L
that occur at least M times.
Formulating Pattern Mining
Find the top K most frequent sets of length at least L
that occur at least M times.
- max_patterns
- min_length
- min_support
Pattern Mining
N distinct items → 2N itemsets
Pattern Mining: Principles
Principle 1: What is frequent?
A pattern is frequent if it occurs at least M times.
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
{C, D}: 5 is frequent
M = 4
{A, D}: 5 is not frequent
Principle 1: What is frequent?
A pattern is frequent if it occurs at least M times.
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
{C, D}: 5 is frequent
M = 4
{A, D}: 5 is not frequent
min_support
Principle 2: Apriori principle
A pattern is frequent only if a subset is frequent
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
{B, C, D} : 5 is frequent therefore
{C, D} : 5 is frequent
{A} : 3 is not frequent therefore
{A, D} : 3 is not frequent
M = 4
Two Main Algorithms
• Candidate Generation
- Apriori
- Eclat
• Pattern Growth
- FP-Growth
- TopK FP-Growth [GLC 1.6]
Lots of Generalizations
Source: http://www.philippe-fournier-viger.com/spmf/
Candidate Generation
Two phases
1. Candidate generation.
2. Candidate filtering.
Exploit Apriori Principle!
Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : ? {B} : ? {C} : ? {D} : ?
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : ? {B} : ? {C} : ? {D} : ?
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : 4 {BD} : 4 {CD} : 5
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Candidate Generation
{AB} : ? {AC} : ? {AD} : ? {BC} : 4 {BD} : 4 {CD} : 5
{A} : 3 {B} : 4 {C} : 5 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{B, C, D}
{A, C, D}
{A, B, C, D}
{A, D}
{B, C, D}
{B, C, D}
Pattern Growth
Two phases
1. Candidate filtering
2. Conditional database constructions.
Avoid full scans over the data & large
candidate sets!
Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : 1 {AC} : 2 {AD} : 3 {BD} : 4 {CD} : 4
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : 0 {ABD} : 1 {ACD} : 2 {BCD} : 2
{BC} : 2
Pattern Growth - Preprocessing {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : ? {AC} : ? {AD} : ? {BD} : ? {CD} : ?
{A} : ? {B} : ? {C} : ? {D} : ?
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : ?
Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : ? {AC} : ? {AD} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : ?
Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : ? {AC} : ? {AD} : ? {BD} : ? {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : ?
Pattern Growth - Depth First {B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{AB} : X {AC} : ? {AD} : ? {BD} : 4 {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : 2
Pattern Growth
{B} : 4
{ } : 6
Call: Growth(db = DB{}, item = B, freq = {B,C,D})
DB{}
{B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
Pattern Growth
{B} : 4
{ } : 6
Conditional Database Construction
DB{} DB{B}
{B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
{C, D}
{D}
{C, D}
{D}
Pattern Growth
{B} : 4
{ } : 6
Candidate Filtering
DB{B}
{C, D}
{D}
{C, D}
{D}
{D} : 4
{C} : 2
DB{}
{B, C, D}
{A, C, D}
{B, D}
{A, C, D}
{B, C, D}
{A, B, D}
DB{B}
Add {BD} as frequent
Pattern Growth - Depth First {C, D}
{D}
{C, D}
{D}
{AB} : X {AC} : ? {AD} : ? {BD} : 4 {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : ? {ACD} : ? {BCD} : ?
{BC} : 2
Pattern Growth
Recurse: Growth(db = DB{B}, item = D, freq = {D})
DB{B}
{C, D}
{D}
{C, D}
{D}
{B} : 4
{ } : 6
{BD} : 4
DB{BD}
Pattern Growth - Depth First
{AB} : X {AC} : ? {AD} : ? {BD} : 4 {CD} : ?
{A} : 3 {B} : 4 {C} : 4 {D} : 6
{ } : 6
{ABC} : ? {ABD} : X {ACD} : ? {BCD} : X
{BC} : 2
Compare & Constrast
• Candidate Generation
+ Better than brute force
+ Filters candidate sets
- Multiple passes over the data
• Pattern Growth
+ Fewer passes over the data
+ Space efficient.
Compare & Constrast
• Candidate Generation
+ Better than brute force
+ Filters candidate sets
- Multiple passes over the data
• Pattern Growth
+ Fewer passes over the data
+ Space efficient.
Better choice
FP-Tree Compression
Figures From Florian Verhein’s Slides on FP-Growth
FP-Growth Algorithm
Figures From Florian Verhein’s Slides on FP-Growth
Two phases
1. Candidate filtering.
2. Conditional database constructions.
TopK FP-Growth Algorithm
Similar to FP-Growth
1. Dynamically raise min_support.
2. Estimates of min_support greatly help.
Performance on Website Logs
• 1.5m events
• 84k sessions
• 3k unique ids
Future Work
Distributed FP-Growth
Partition database on item-ids.
Database
Bags + Sequences
× 2
Itemset: {Item}
Bags: {Item: quantity}
Sequences : (item)
Model built, now what?
Creating a model pipeline
Ingest Transform Model Deploy
Unstructured Data
exploration
data
modeling
Data Science Workflow
Ingest Transform Model Deploy
Demo
Summary
Log Data Mining
≠
Rocket Science
• FP-Growth for finding frequent patterns.
• Find rules from patterns to make predictions.
• Extract features for useful ML in pattern space.
SELECT questions FROM audience
WHERE difficulty == “Easy”
Thanks!
Extra Slides

Contenu connexe

En vedette

Product life cycle & marketing strategy
Product life cycle & marketing strategyProduct life cycle & marketing strategy
Product life cycle & marketing strategyHitesh Sunny
 
10 Ways to Improve Internal Communication
10 Ways to Improve Internal Communication10 Ways to Improve Internal Communication
10 Ways to Improve Internal CommunicationWeekdone.com
 
Congratulations Graduate! Eleven Reasons Why I Will Never Hire You.
Congratulations Graduate! Eleven Reasons Why I Will Never Hire You.Congratulations Graduate! Eleven Reasons Why I Will Never Hire You.
Congratulations Graduate! Eleven Reasons Why I Will Never Hire You.Mark O'Toole
 
Product life cycle & marketing strategies
Product life cycle & marketing strategiesProduct life cycle & marketing strategies
Product life cycle & marketing strategiesAmar Ingale
 
XII Marketing Project Work
XII Marketing Project WorkXII Marketing Project Work
XII Marketing Project WorkRahil Jain
 
The Little Book of IDEO: Values
The Little Book of IDEO: ValuesThe Little Book of IDEO: Values
The Little Book of IDEO: ValuesTim Brown
 
Eagle from eBay at China Hadoop Summit 2015
Eagle from eBay at China Hadoop Summit 2015Eagle from eBay at China Hadoop Summit 2015
Eagle from eBay at China Hadoop Summit 2015Hao Chen
 
Developing Distributed Web Applications, Where does REST fit in?
Developing Distributed Web Applications, Where does REST fit in?Developing Distributed Web Applications, Where does REST fit in?
Developing Distributed Web Applications, Where does REST fit in?Srinath Perera
 
Hadoop Eagle - Real Time Monitoring Framework for eBay Hadoop
Hadoop Eagle - Real Time Monitoring Framework for eBay HadoopHadoop Eagle - Real Time Monitoring Framework for eBay Hadoop
Hadoop Eagle - Real Time Monitoring Framework for eBay HadoopDataWorks Summit
 
Siddhi: A Second Look at Complex Event Processing Implementations
Siddhi: A Second Look at Complex Event Processing ImplementationsSiddhi: A Second Look at Complex Event Processing Implementations
Siddhi: A Second Look at Complex Event Processing ImplementationsSrinath Perera
 
From Beginners to Experts, Data Wrangling for All
From Beginners to Experts, Data Wrangling for AllFrom Beginners to Experts, Data Wrangling for All
From Beginners to Experts, Data Wrangling for AllDataWorks Summit
 
Ahead Week 1 Key Slides
Ahead Week 1 Key SlidesAhead Week 1 Key Slides
Ahead Week 1 Key Slidesaltonbaird
 
Animales en peligro de extincion
Animales en peligro de extincionAnimales en peligro de extincion
Animales en peligro de extincionlosdonkey
 
BNI Lake Business Builders- LOZ Vice President report
BNI Lake Business Builders- LOZ Vice President reportBNI Lake Business Builders- LOZ Vice President report
BNI Lake Business Builders- LOZ Vice President reportMike Tobin
 
Setting up Your LinkedIn Account
Setting up Your LinkedIn AccountSetting up Your LinkedIn Account
Setting up Your LinkedIn AccountNET:101
 

En vedette (20)

Types of brand
Types of brandTypes of brand
Types of brand
 
Product life cycle & marketing strategy
Product life cycle & marketing strategyProduct life cycle & marketing strategy
Product life cycle & marketing strategy
 
10 Ways to Improve Internal Communication
10 Ways to Improve Internal Communication10 Ways to Improve Internal Communication
10 Ways to Improve Internal Communication
 
Congratulations Graduate! Eleven Reasons Why I Will Never Hire You.
Congratulations Graduate! Eleven Reasons Why I Will Never Hire You.Congratulations Graduate! Eleven Reasons Why I Will Never Hire You.
Congratulations Graduate! Eleven Reasons Why I Will Never Hire You.
 
Product life cycle & marketing strategies
Product life cycle & marketing strategiesProduct life cycle & marketing strategies
Product life cycle & marketing strategies
 
XII Marketing Project Work
XII Marketing Project WorkXII Marketing Project Work
XII Marketing Project Work
 
The Little Book of IDEO: Values
The Little Book of IDEO: ValuesThe Little Book of IDEO: Values
The Little Book of IDEO: Values
 
Death by PowerPoint
Death by PowerPointDeath by PowerPoint
Death by PowerPoint
 
Culture
CultureCulture
Culture
 
Slideshare ppt
Slideshare pptSlideshare ppt
Slideshare ppt
 
Eagle from eBay at China Hadoop Summit 2015
Eagle from eBay at China Hadoop Summit 2015Eagle from eBay at China Hadoop Summit 2015
Eagle from eBay at China Hadoop Summit 2015
 
Developing Distributed Web Applications, Where does REST fit in?
Developing Distributed Web Applications, Where does REST fit in?Developing Distributed Web Applications, Where does REST fit in?
Developing Distributed Web Applications, Where does REST fit in?
 
Hadoop Eagle - Real Time Monitoring Framework for eBay Hadoop
Hadoop Eagle - Real Time Monitoring Framework for eBay HadoopHadoop Eagle - Real Time Monitoring Framework for eBay Hadoop
Hadoop Eagle - Real Time Monitoring Framework for eBay Hadoop
 
Siddhi: A Second Look at Complex Event Processing Implementations
Siddhi: A Second Look at Complex Event Processing ImplementationsSiddhi: A Second Look at Complex Event Processing Implementations
Siddhi: A Second Look at Complex Event Processing Implementations
 
From Beginners to Experts, Data Wrangling for All
From Beginners to Experts, Data Wrangling for AllFrom Beginners to Experts, Data Wrangling for All
From Beginners to Experts, Data Wrangling for All
 
Apache Eagle - Monitor Hadoop in Real Time
Apache Eagle - Monitor Hadoop in Real TimeApache Eagle - Monitor Hadoop in Real Time
Apache Eagle - Monitor Hadoop in Real Time
 
Ahead Week 1 Key Slides
Ahead Week 1 Key SlidesAhead Week 1 Key Slides
Ahead Week 1 Key Slides
 
Animales en peligro de extincion
Animales en peligro de extincionAnimales en peligro de extincion
Animales en peligro de extincion
 
BNI Lake Business Builders- LOZ Vice President report
BNI Lake Business Builders- LOZ Vice President reportBNI Lake Business Builders- LOZ Vice President report
BNI Lake Business Builders- LOZ Vice President report
 
Setting up Your LinkedIn Account
Setting up Your LinkedIn AccountSetting up Your LinkedIn Account
Setting up Your LinkedIn Account
 

Similaire à Pattern Mining: Extracting Value from Log Data

Frequent Pattern Mining - Krishna Sridhar, Feb 2016
Frequent Pattern Mining - Krishna Sridhar, Feb 2016Frequent Pattern Mining - Krishna Sridhar, Feb 2016
Frequent Pattern Mining - Krishna Sridhar, Feb 2016Seattle DAML meetup
 
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16MLconf
 
Massively distributed environments and closed itemset mining
Massively distributed environments and closed itemset miningMassively distributed environments and closed itemset mining
Massively distributed environments and closed itemset miningMehdi Zitouni
 
Outrageous Ideas for Graph Databases
Outrageous Ideas for Graph DatabasesOutrageous Ideas for Graph Databases
Outrageous Ideas for Graph DatabasesMax De Marzi
 
Developer Intro Deck-PowerPoint - Download for Speaker Notes
Developer Intro Deck-PowerPoint - Download for Speaker NotesDeveloper Intro Deck-PowerPoint - Download for Speaker Notes
Developer Intro Deck-PowerPoint - Download for Speaker NotesMax De Marzi
 
Consistency without Consensus: CRDTs in Production at SoundCloud
Consistency without Consensus: CRDTs in Production at SoundCloudConsistency without Consensus: CRDTs in Production at SoundCloud
Consistency without Consensus: CRDTs in Production at SoundCloudC4Media
 
Realizability Analysis for Message-based Interactions Using Shared-State Proj...
Realizability Analysis for Message-based Interactions Using Shared-State Proj...Realizability Analysis for Message-based Interactions Using Shared-State Proj...
Realizability Analysis for Message-based Interactions Using Shared-State Proj...Sylvain Hallé
 
Relationship & function
Relationship & functionRelationship & function
Relationship & functionmeraskill
 
Optimizing the Catalyst Optimizer for Complex Plans
Optimizing the Catalyst Optimizer for Complex PlansOptimizing the Catalyst Optimizer for Complex Plans
Optimizing the Catalyst Optimizer for Complex PlansDatabricks
 
Visual Api Training
Visual Api TrainingVisual Api Training
Visual Api TrainingSpark Summit
 
Module 5 Indices PMR
Module 5 Indices PMRModule 5 Indices PMR
Module 5 Indices PMRroszelan
 
Analysis of Tree in Computer Based Application
Analysis of Tree in Computer Based ApplicationAnalysis of Tree in Computer Based Application
Analysis of Tree in Computer Based ApplicationNazmul Hyder
 
267 4 determinant and cross product-n
267 4 determinant and cross product-n267 4 determinant and cross product-n
267 4 determinant and cross product-nmath260
 
Advertising
AdvertisingAdvertising
Advertisingblack150
 

Similaire à Pattern Mining: Extracting Value from Log Data (20)

Frequent Pattern Mining - Krishna Sridhar, Feb 2016
Frequent Pattern Mining - Krishna Sridhar, Feb 2016Frequent Pattern Mining - Krishna Sridhar, Feb 2016
Frequent Pattern Mining - Krishna Sridhar, Feb 2016
 
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
 
Massively distributed environments and closed itemset mining
Massively distributed environments and closed itemset miningMassively distributed environments and closed itemset mining
Massively distributed environments and closed itemset mining
 
My6asso
My6assoMy6asso
My6asso
 
Outrageous Ideas for Graph Databases
Outrageous Ideas for Graph DatabasesOutrageous Ideas for Graph Databases
Outrageous Ideas for Graph Databases
 
Developer Intro Deck-PowerPoint - Download for Speaker Notes
Developer Intro Deck-PowerPoint - Download for Speaker NotesDeveloper Intro Deck-PowerPoint - Download for Speaker Notes
Developer Intro Deck-PowerPoint - Download for Speaker Notes
 
Mongo indexes
Mongo indexesMongo indexes
Mongo indexes
 
Amazon DynamoDB Workshop
Amazon DynamoDB WorkshopAmazon DynamoDB Workshop
Amazon DynamoDB Workshop
 
Consistency without Consensus: CRDTs in Production at SoundCloud
Consistency without Consensus: CRDTs in Production at SoundCloudConsistency without Consensus: CRDTs in Production at SoundCloud
Consistency without Consensus: CRDTs in Production at SoundCloud
 
Realizability Analysis for Message-based Interactions Using Shared-State Proj...
Realizability Analysis for Message-based Interactions Using Shared-State Proj...Realizability Analysis for Message-based Interactions Using Shared-State Proj...
Realizability Analysis for Message-based Interactions Using Shared-State Proj...
 
6asso
6asso6asso
6asso
 
Relationship & function
Relationship & functionRelationship & function
Relationship & function
 
Optimizing the Catalyst Optimizer for Complex Plans
Optimizing the Catalyst Optimizer for Complex PlansOptimizing the Catalyst Optimizer for Complex Plans
Optimizing the Catalyst Optimizer for Complex Plans
 
Visual Api Training
Visual Api TrainingVisual Api Training
Visual Api Training
 
Module 5 Indices PMR
Module 5 Indices PMRModule 5 Indices PMR
Module 5 Indices PMR
 
Analysis of Tree in Computer Based Application
Analysis of Tree in Computer Based ApplicationAnalysis of Tree in Computer Based Application
Analysis of Tree in Computer Based Application
 
Data Mining Lecture_4.pptx
Data Mining Lecture_4.pptxData Mining Lecture_4.pptx
Data Mining Lecture_4.pptx
 
Data Mining.ppt
Data Mining.pptData Mining.ppt
Data Mining.ppt
 
267 4 determinant and cross product-n
267 4 determinant and cross product-n267 4 determinant and cross product-n
267 4 determinant and cross product-n
 
Advertising
AdvertisingAdvertising
Advertising
 

Plus de Turi, Inc.

Webinar - Patient Readmission Risk
Webinar - Patient Readmission RiskWebinar - Patient Readmission Risk
Webinar - Patient Readmission RiskTuri, Inc.
 
Webinar - Know Your Customer - Arya (20160526)
Webinar - Know Your Customer - Arya (20160526)Webinar - Know Your Customer - Arya (20160526)
Webinar - Know Your Customer - Arya (20160526)Turi, Inc.
 
Webinar - Product Matching - Palombo (20160428)
Webinar - Product Matching - Palombo (20160428)Webinar - Product Matching - Palombo (20160428)
Webinar - Product Matching - Palombo (20160428)Turi, Inc.
 
Webinar - Fraud Detection - Palombo (20160428)
Webinar - Fraud Detection - Palombo (20160428)Webinar - Fraud Detection - Palombo (20160428)
Webinar - Fraud Detection - Palombo (20160428)Turi, Inc.
 
Scaling Up Machine Learning: How to Benchmark GraphLab Create on Huge Datasets
Scaling Up Machine Learning: How to Benchmark GraphLab Create on Huge DatasetsScaling Up Machine Learning: How to Benchmark GraphLab Create on Huge Datasets
Scaling Up Machine Learning: How to Benchmark GraphLab Create on Huge DatasetsTuri, Inc.
 
Text Analysis with Machine Learning
Text Analysis with Machine LearningText Analysis with Machine Learning
Text Analysis with Machine LearningTuri, Inc.
 
Machine Learning with GraphLab Create
Machine Learning with GraphLab CreateMachine Learning with GraphLab Create
Machine Learning with GraphLab CreateTuri, Inc.
 
Machine Learning in Production with Dato Predictive Services
Machine Learning in Production with Dato Predictive ServicesMachine Learning in Production with Dato Predictive Services
Machine Learning in Production with Dato Predictive ServicesTuri, Inc.
 
Machine Learning in 2016: Live Q&A with Carlos Guestrin
Machine Learning in 2016: Live Q&A with Carlos GuestrinMachine Learning in 2016: Live Q&A with Carlos Guestrin
Machine Learning in 2016: Live Q&A with Carlos GuestrinTuri, Inc.
 
Scalable data structures for data science
Scalable data structures for data scienceScalable data structures for data science
Scalable data structures for data scienceTuri, Inc.
 
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015Turi, Inc.
 
Introduction to Recommender Systems
Introduction to Recommender SystemsIntroduction to Recommender Systems
Introduction to Recommender SystemsTuri, Inc.
 
Machine learning in production
Machine learning in productionMachine learning in production
Machine learning in productionTuri, Inc.
 
Overview of Machine Learning and Feature Engineering
Overview of Machine Learning and Feature EngineeringOverview of Machine Learning and Feature Engineering
Overview of Machine Learning and Feature EngineeringTuri, Inc.
 
Building Personalized Data Products with Dato
Building Personalized Data Products with DatoBuilding Personalized Data Products with Dato
Building Personalized Data Products with DatoTuri, Inc.
 
Getting Started With Dato - August 2015
Getting Started With Dato - August 2015Getting Started With Dato - August 2015
Getting Started With Dato - August 2015Turi, Inc.
 
Towards a Comprehensive Machine Learning Benchmark
Towards a Comprehensive Machine Learning BenchmarkTowards a Comprehensive Machine Learning Benchmark
Towards a Comprehensive Machine Learning BenchmarkTuri, Inc.
 
New Capabilities in the PyData Ecosystem
New Capabilities in the PyData EcosystemNew Capabilities in the PyData Ecosystem
New Capabilities in the PyData EcosystemTuri, Inc.
 

Plus de Turi, Inc. (20)

Webinar - Patient Readmission Risk
Webinar - Patient Readmission RiskWebinar - Patient Readmission Risk
Webinar - Patient Readmission Risk
 
Webinar - Know Your Customer - Arya (20160526)
Webinar - Know Your Customer - Arya (20160526)Webinar - Know Your Customer - Arya (20160526)
Webinar - Know Your Customer - Arya (20160526)
 
Webinar - Product Matching - Palombo (20160428)
Webinar - Product Matching - Palombo (20160428)Webinar - Product Matching - Palombo (20160428)
Webinar - Product Matching - Palombo (20160428)
 
Webinar - Fraud Detection - Palombo (20160428)
Webinar - Fraud Detection - Palombo (20160428)Webinar - Fraud Detection - Palombo (20160428)
Webinar - Fraud Detection - Palombo (20160428)
 
Scaling Up Machine Learning: How to Benchmark GraphLab Create on Huge Datasets
Scaling Up Machine Learning: How to Benchmark GraphLab Create on Huge DatasetsScaling Up Machine Learning: How to Benchmark GraphLab Create on Huge Datasets
Scaling Up Machine Learning: How to Benchmark GraphLab Create on Huge Datasets
 
Text Analysis with Machine Learning
Text Analysis with Machine LearningText Analysis with Machine Learning
Text Analysis with Machine Learning
 
Machine Learning with GraphLab Create
Machine Learning with GraphLab CreateMachine Learning with GraphLab Create
Machine Learning with GraphLab Create
 
Machine Learning in Production with Dato Predictive Services
Machine Learning in Production with Dato Predictive ServicesMachine Learning in Production with Dato Predictive Services
Machine Learning in Production with Dato Predictive Services
 
Machine Learning in 2016: Live Q&A with Carlos Guestrin
Machine Learning in 2016: Live Q&A with Carlos GuestrinMachine Learning in 2016: Live Q&A with Carlos Guestrin
Machine Learning in 2016: Live Q&A with Carlos Guestrin
 
Scalable data structures for data science
Scalable data structures for data scienceScalable data structures for data science
Scalable data structures for data science
 
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
Introduction to Deep Learning for Image Analysis at Strata NYC, Sep 2015
 
Introduction to Recommender Systems
Introduction to Recommender SystemsIntroduction to Recommender Systems
Introduction to Recommender Systems
 
Machine learning in production
Machine learning in productionMachine learning in production
Machine learning in production
 
Overview of Machine Learning and Feature Engineering
Overview of Machine Learning and Feature EngineeringOverview of Machine Learning and Feature Engineering
Overview of Machine Learning and Feature Engineering
 
SFrame
SFrameSFrame
SFrame
 
Building Personalized Data Products with Dato
Building Personalized Data Products with DatoBuilding Personalized Data Products with Dato
Building Personalized Data Products with Dato
 
Getting Started With Dato - August 2015
Getting Started With Dato - August 2015Getting Started With Dato - August 2015
Getting Started With Dato - August 2015
 
Towards a Comprehensive Machine Learning Benchmark
Towards a Comprehensive Machine Learning BenchmarkTowards a Comprehensive Machine Learning Benchmark
Towards a Comprehensive Machine Learning Benchmark
 
Dato Keynote
Dato KeynoteDato Keynote
Dato Keynote
 
New Capabilities in the PyData Ecosystem
New Capabilities in the PyData EcosystemNew Capabilities in the PyData Ecosystem
New Capabilities in the PyData Ecosystem
 

Dernier

Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAshyamraj55
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8DianaGray10
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarPrecisely
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7DianaGray10
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationIES VE
 
Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxGDSC PJATK
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxMatsuo Lab
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1DianaGray10
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfJamie (Taka) Wang
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UbiTrack UK
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsSeth Reyes
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.YounusS2
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPathCommunity
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesDavid Newbury
 
Videogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfVideogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfinfogdgmi
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDELiveplex
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxUdaiappa Ramachandran
 

Dernier (20)

Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity Webinar
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
 
Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptx
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptx
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
 
20150722 - AGV
20150722 - AGV20150722 - AGV
20150722 - AGV
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and Hazards
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.
 
UiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation DevelopersUiPath Community: AI for UiPath Automation Developers
UiPath Community: AI for UiPath Automation Developers
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond Ontologies
 
Videogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdfVideogame localization & technology_ how to enhance the power of translation.pdf
Videogame localization & technology_ how to enhance the power of translation.pdf
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptx
 

Pattern Mining: Extracting Value from Log Data