IEEE 2014 JAVA DATA MINING PROJECTS Active learning of constraints for semi supervised clustering
- 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com 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.