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Multiobjective Clustering with Automatic k-determination for Large-scale Data

Multiobjective Clustering with Automatic k-determination for Large-scale Data. Presenter : Shao-Wei Cheng Authors : Nobukazu Matake, Tomoyuki Hiroyasu, Mitsunori Miki, Tomoharu Senda. CECCO 2007. Outline. Motivation Objective Methodology Original MOCK

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Multiobjective Clustering with Automatic k-determination for Large-scale Data

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  1. Multiobjective Clustering with Automatic k-determination for Large-scale Data Presenter : Shao-Wei Cheng Authors : Nobukazu Matake, Tomoyuki Hiroyasu, Mitsunori Miki, Tomoharu Senda CECCO 2007

  2. Outline • Motivation • Objective • Methodology • Original MOCK • New scalable k-determination scheme • Experiments and Results • Conclusion • Personal Comments

  3. Motivation • Web behavior mining has attracted a great deal of attention today. • MOCK is powerful and strict. But the computational costs are too high when applied to clustering huge data. Too Much Data !!

  4. Objectives • Apply MOCK to web data clustering with a scalable automatic k-determination scheme. • Determine the appropriate k at low cost. • It contains two complementary objectives. • Determination of appropriate k. • Find partitions between k clusters.

  5. Methodology • Original MOCK Third Step First Step Forth Step Second Step Gap statistic

  6. Methodology • New scalable k-determination scheme First Step Second Step First scheme：Calculate adjacent angles x y Second scheme x x

  7. Experiments

  8. Conclusion • The new scheme is able to determine the appropriate k at low cost, although the performance is poorer than the original algorithm. • Reduce the Pareto size by about 50-70%. • Doesn’t need random data clustering.

  9. Personal Comments • Advantage • MOCK can be applied to large-scale data. • Drawback • Application • Web data.

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