This document summarizes a paper on active learning for semi-supervised clustering. It introduces a novel approach for computing uncertainty in data points and selecting queries that have the highest information rate. The proposed method trades off uncertainty and query cost to iteratively expand neighborhoods defined by pairwise constraints. Evaluation on benchmark datasets demonstrates substantial improvements over the state of the art.

1. GLOBALSOFT TECHNOLOGIES
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Active Learning Of Constraints For Semi-Supervised
Clustering
ABSTRACT:
In this paper, we study the active learning problem of selecting pair wise must- link and cannot-link
constraints for semi supervised clustering. We consider active learning in an iterative
manner where in each iterations queries are selected based on the current clustering solution and
the existing constraint set. We apply a general framework that builds on the concept of
neighborhood, where neighborhoods contain “labeled examples” of different clusters according
to the pair wise constraints.
Our active learning method expands the neighborhoods by selecting informative points and
querying their relationship with the neighborhoods. Under this framework, we build on the
classic uncertainty-based principle and present a novel approach for computing the uncertainty
associated with each data point.
We further introduce a selection criterion that trades off the amount of uncertainty of each data
point with the expected number of queries (the cost) required to resolve this uncertainty. This
allows us to select queries that have the highest information rate. We evaluate the proposed
method on the benchmark data sets and the results demonstrate consistent and substantial
improvements over the current state of the art.

2. Existing System
we study the active learning problem of selecting pair wise must- link and cannot- link constraints
for semi supervised clustering. We consider active learning in an iterative manner where in each
iterations queries are selected based on the current clustering solution and the existing constraint
set. We apply a general framework that builds on the concept of neighborhood, where
neighborhoods contain “labeled examples” of different clusters according to the pair wise
constraints.
Our active learning method expands the neighborhoods by selecting informative points and
querying their relationship with the neighborhoods. Under this framework, we build on the
classic uncertainty-based principle and present a novel approach for computing the uncertainty
associated with each data point.
Proposed System
We further introduce a selection criterion that trades off the amount of uncertainty of
each data point with the expected number of queries (the cost) required to resolve this
uncertainty. This allows us to select queries that have the highest information rate. We evaluate
the proposed method on the benchmark data sets and the results demonstrate consistent and
substantial improvements over the current state of the art.

3. System Configuration:-
Hardware Configuration:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
Software Configuration:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.