Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Extensions of vector quantization for incremental clustering Author: Edwin Lughofer Reporter: Wen-Cheng Tsai 2008/01/25 1 PR, 2008

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline  Motivation  Objective  Method ─ Vector quantization (VQ) ─ Vector quantization in incremental mode (VQ-INC) ─ Extended version of vector quantization in incremental model (VQ- INC-EXT) ─ Satellite deletion ─ Split –and-merge strategy  Experiments  Conclusions  Comments 2

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation  In nowadays industrial system a special emphasis is given on incremental clustering processes. This is because in various applications quite often online measurements are recorded resulting in data streams.  Hence, the data streams must generally be processed in online manner to guarantee that results are up-data and that queries can be answered with a small time delay. 3

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective  We extend the conventional vector quantization by incorporating a vigilance parameter, which steers the tradeoff between plasticity and stability incremental online learning.  It also omit the pre-definition of the number of clusters, which has to be sent as parameter into conventional vector quantization.  A satellite deletion strategy is proposed to remove not significant clusters after the complete learning process.  A split-and-merge strategy is described, which guides the incremental clustering process to cluster partitions with a high quality, even though an undesired setting of the most essential vigilance parameter was carried out in advance. 4

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Vector quantization (VQ) 5

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Vector quantization in incremental mode (VQ-INC) 6

Intelligent Database Systems Lab N.Y.U.S.T. I. M. VQ-INC-EXT 7

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Satellite deletion 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Split –and-merge strategy 11

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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments – results on clustering data sets 13

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Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments – evaluation on image classification based on cluster extraction in the feature space 15

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusions  A new extended version of vector quantization (VQ-INC-EXT) was proposed. It extends conventional vector quantization to an incremental variant and incorporates a new distance strategy, which takes into account the range of influence of cluster.  A satellite deletion strategy is for removing not really significant clusters.  Split-and –merge strategy guides the incremental clustering process to more accurate cluster partitions, whenever a badly a priori setting of the vigilance parameter p was carried out. 16

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments  Advantages ─ …  Disadvantages ─ …  Application ─ Clustering 17