unsupervised evolutionary clustering algorithm for mixed type data

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

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Unsupervised Evolutionary Clustering Algorithm for Mixed Type Data

Zhi Zheng , Maoguo Gong , Jingjing Ma , Licheng Jiao , Qiaodi Wu2010,CEC

Presented by Chien-Hao Kung2011/12/1


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Outlines· Motivation· Objectives· Methodology· Experiments· Conclusions· Comments


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Motivation

· As a partitional clustering algorithm, K-prototype (KP) algorithm is a well-known one for mixed type data.

· However, it is sensitive to initialization and converges to local optimum easily.


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Objectives

· In this study, KP is applied as a local search strategy, and runs under the Global searching to help KP overcome its flaws.


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· K-prototype Algorithm─ Step1.Initializing.─ Step2.For each data item, calculating the distances.

─ Step3.Retest every data item.─ Step4.Repeat Step3. until no item changes its cluster.

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· Evolutionary k-prototype(EKP)─ Step1 Initialization.─ Step2 Crossover.─ Step3 Mutation.─ Step4 KP Search.─ Step5 Evaluation and Selection.─ Step6 Termination Test.

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─ There are 8 parameters have to be set before evolution. Cluster number r is a weight in EKP which balance the influence on clustering Population size Proportion of initial individuals that generated by choosing

items randomly in dataset (IP) Crossover probability Mutation probability in simulated binary crossover(SBX) n in polynomial mutation

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· Initialization─ Two kinds of random initialization schemes

─ The first is randomly choosing K data item as the prototypes of clusters

─ The second is randomly generating K prototypes

Ex:

[2.23,5.63],[6.56,5.13], and {1,2,3,4,5,6},{2,4}

=>{3.21,6.23,2,4}


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· Crossover.─ Numerical type --Simulated binary crossover(SBX)─ Categorical type – Single point crossover


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· Mutation


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· KP Search· Evaluation and Selection

· Termination Test


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· Parameter setting

Experiments

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· Dataset


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Conclusions· This paper propose a novel unsupervised clustering

algorithm for mixed type data named evolutionary k-prototype(EKP) .

· The experiment result show that the evolutionary framework improves the original algorithms markedly.

· EKP which can adjust this weight automatically needs to be studied.


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Comments

· Drawback─ This method use the parameter too much.

Application─ Clustering

unsupervised evolutionary clustering algorithm for mixed type data

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