The impact of semi-supervised clustering on text classification
作者： Antonia Kyriakopoulou , Theodore Kalamboukis
关键词: Computer science 、 Machine learning 、 CURE data clustering algorithm 、 Brown clustering 、 Correlation clustering 、 Pattern recognition 、 Clustering high-dimensional data 、 Conceptual clustering 、 Canopy clustering algorithm 、 Fuzzy clustering 、 Cluster analysis 、 Artificial intelligence
摘要: This paper addresses the problem of learning to classify texts by exploiting information derived from clustering both training and testing sets. The incorporation knowledge resulting into feature space representation is expected boost performance a classifier. Two different approaches are described, an unsupervised semi-supervised one. We present empirical study proposed algorithms on variety datasets. results encouraging, revealing that can create text classifiers high-accuracy.
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