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Document Clustering With Dual Supervision Through Feature Reweighting
Author(s) -
Hu Yeming,
Milios Evangelos E.,
Blustein James
Publication year - 2016
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12064
Subject(s) - cluster analysis , computer science , feature (linguistics) , brown clustering , artificial intelligence , pairwise comparison , correlation clustering , pattern recognition (psychology) , fuzzy clustering , consensus clustering , data mining , conceptual clustering , machine learning , canopy clustering algorithm , philosophy , linguistics
Traditional semi‐supervised clustering uses only limited user supervision in the form of instance seeds for clusters and pairwise instance constraints to aid unsupervised clustering. However, user supervision can also be provided in alternative forms for document clustering, such as labeling a feature by indicating whether it discriminates among clusters. This article thus fills this void by enhancing traditional semi‐supervised clustering with feature supervision, which asks the user to label discriminating features during defining (labeling) the instance seeds or pairwise instance constraints. Various types of semi‐supervised clustering algorithms were explored with feature supervision. Our experimental results on several real‐world data sets demonstrate that augmenting the instance‐level supervision with feature‐level supervision can significantly improve document clustering performance.