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Mining Stream, Time Series, and Sequence Data,[object Object]
Methodologies for Stream Data Processing and Stream Data Systems,[object Object],Random Sampling,[object Object],Sliding Windows,[object Object],Histograms,[object Object],Multi resolution Methods,[object Object],Sketches Synopses,[object Object]
Randomized Algorithms to analyze Data Streams,[object Object],Randomized algorithms, in the form of random sampling and sketching, are often used to deal with massive, high-dimensional data streams.,[object Object]
Data Stream Management Systems and Stream Queries,[object Object],In traditional database systems, data are stored in finite and persistent databases.,[object Object],stream data are infinite and impossible to store fully in a database.,[object Object], Data Stream Management System (DSMS), there may be multiple data streams.,[object Object],Once an element from a data stream has been processed, it is discarded or archived, and it cannot be easily retrieved unless it is explicitly stored in memory,[object Object]
Critical Layers of stream data cube,[object Object],    Two critical cuboids (or layers),[object Object],The first layer, called the minimal interest layer, is the minimally interesting layer that ananalyst would like to study,[object Object],The second layer, called the observation layer, is the layer at which an analyst (or anautomated system) would like to continuously study the data.,[object Object]
Hoeffding Tree Algorithm,[object Object],The Hoeffding tree algorithm is a decision tree learning method for stream data classification.,[object Object],It was initially used to track Web click streams and construct models to predict which Web hosts and Web sites a user is likely to access. ,[object Object],It typically runs in sublinear time and produces a nearly identical decision tree to that of traditional batch learners.,[object Object],It uses Hoeffding trees, which exploit the idea that a small sample can often be enough to choose an optimal splitting attribute. ,[object Object]
Very Fast Decision Tree (VFDT) ,[object Object],The VFDT (Very Fast Decision Tree) algorithm makes several modifications to the Hoeffding tree algorithm.,[object Object],The modifications include breaking near-ties during attribute selection more aggressively, computing the G function after a number of training examples, deactivating the least promising leaves whenever memory is running low, dropping poor splitting attributes, and improving the initialization method.,[object Object],VFDT works well on stream data and also compares extremely well to traditional classifiers in both speed and accuracy To adapt to concept-drifting data streams.,[object Object]
Concept-adapting Very Fast Decision Tree algorithm (CVFDT).,[object Object],CVFDT also uses a sliding window approach; ,[object Object],however, it does not construct a new model from scratch each time. Rather, it updates statistics at the nodes by incrementing the counts associated with new examples and decrementing the counts associated with old ones. ,[object Object],Therefore, if there is a concept drift, some nodes may no longer pass the Hoeffding bound. When this happens, an alternate subtree will be grown, with the new best splitting attribute at the root.,[object Object]
A Classifier Ensemble Approach to Stream Data Classification,[object Object],The idea is to train an ensemble or group of classifiers (using, say naïve Bayes) from sequential chunks of the data stream.,[object Object],Whenever a new chunk arrives, we build a new classifier from it. ,[object Object],The individual classifiers are weighted based on their expected classification accuracy in a time-changing environment. ,[object Object],Only the top-k classifiers are kept. The decisions are then based on the weighted votes of the classifiers.,[object Object]
Clustering in evolving data streams,[object Object],Compute and store summaries of past data,[object Object],Apply a divide-and-conquer strategy,[object Object],Incremental clustering of incoming data streams,[object Object],Perform micro clustering as well as macro clustering analysis,[object Object],Explore multiple time granularity for the analysis of cluster evolution,[object Object],Divide stream clustering into on-line and off-line processes,[object Object]
Mining Time-Series Data,[object Object],A time-series database consists of sequences of values or events obtained over repeated measurements of time.,[object Object],Trend Analysis,[object Object],Similarity Search in Time-Series Analysis,[object Object]
Markov Chain for sequence analysis,[object Object],A Markov chain is a model that generates sequences in which the probability of a symbol depends only on the previous symbol.,[object Object]
Tasks using hidden Markov models include:,[object Object],Evaluation: Given a sequence, x, determine the probability, P(x), of obtaining x in the model.,[object Object],Decoding: Given a sequence, determine the most probable path through the model that produced the sequence.,[object Object],Learning: Given a model and a set of training sequences, find the model parameters (i.e., the transition and emission probabilities) that explain the training sequences with relatively high probability.,[object Object]
Different algorithms in series analysis,[object Object],Forward Algorithm,[object Object],Viterbi Algorithm,[object Object],Baum-Welch Algorithm,[object Object]
Visit more self help tutorials,[object Object],Pick a tutorial of your choice and browse through it at your own pace.,[object Object],The tutorials section is free, self-guiding and will not involve any additional support.,[object Object],Visit us at www.dataminingtools.net,[object Object]

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Data Mining: Mining stream time series and sequence data

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