Generalized clustering, supervised learning, and data assignment

Clustering algorithms have become increasingly important in handling and analyzing data. Considerable work has been done in devising effective but increasingly specific clustering algorithms. In contrast, we have developed a generalized framework that accommodates diverse clustering algorithms in a systematic way. This framework views clustering as a general process of iterative optimization that includes modules for supervised learning and instance assignment. This framework has also suggested several novel clustering methods. In this paper, we investigate experimentally the efficacy of these algorithms and test some hypotheses about the relation between such unsupervised techniques and the supervised methods embedded in them. Categories and Subject Descriptors I.2.6 [Computing Methodologies]: Artificial Intelligence-Learning General Terms Algorithms, design, experimentation Keywords Clustering, supervised learning, iterative optimization

• Type: Conference
• Year: 2001
• Where: KDD
• Authors: Annaka Kalton, Pat Langley, Kiri Wagstaff, Jungsoon P. Yoo
• Keywords: Data Mining, Diverse Clustering Algorithms, General Terms Algorithms, KDD 2001, Specific Clustering Algorithms