Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
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In this paper we present a method to cluster large datasets that change over time using incremental learning techniques. The approach is based on the dynamic representation of clusters that involves the use of two sets of representative points which are used to capture both the current shape of the cluster as well as the trend and type of change occuring in the data. The processing is done in an incremental point by point fashion and combines both data prediction and past history analysis to classify the unlabeled data. We present the results obtained using several datasets and compare the performance with the well known clustering algorithm CURE.