Scalable Hierarchical Clustering Method for Sequences of Categorical Values

Data clustering methods have many applications in the area of data mining. Traditional clustering algorithms deal with quantitative or categorical data points. However, there exist many important databases that store categorical data sequences, where significant knowledge is hidden behind sequential dependencies between the data. In this paper we introduce a problem of clustering categorical data sequences and present an efficient scalable algorithm to solve the problem. Our algorithm implements the general idea of agglomerative hierarchical clustering and uses frequently occurring subsequences as features describing data sequences. The algorithm not only discovers a set of high quality clusters containing similar data sequences but also provides descriptions of the discovered clusters.

• Type: Conference
• Year: 2001
• Where: PAKDD
• Authors: Tadeusz Morzy, Marek Wojciechowski, Maciej Zakrzewicz
• Keywords: Categorical Data, Categorical Data Sequences, Data Mining, Data Sequences, PAKDD 2001