Presenter : Keng -Yu Lin Author : Amir Ahmad , Lipika Dey PRL . 2011

Intelligent Database Systems Lab

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

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A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets

Presenter : Keng-Yu LinAuthor : Amir Ahmad , Lipika Dey

PRL. 2011


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I. M.

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


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I. M.Motivation· Almost all subspace clustering algorithms proposed so far

are designed for numeric datasets.

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Objectives· This paper present a k-means type clustering algorithm

that finds clusters in data subspaces in mixed numeric and categorical datasets.


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I. M.Methodology· k-means clustering algorithm

1. Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.

2. Assign each object to the group that has the closest centroid.

3. When all objects have been assigned, recalculate the positions of the K centroids.

4. Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized can be calculated.

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I. M.Methodology

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I. M.Experiments· Vote dataset

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error rate : 4.8%Zaki et al. error rate : 3.8%


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I. M.Experiments· Mushroom datasets

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error rate : 4.1%Zaki et al. error rate : 0.3%


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I. M.Experiments· DNA datasets

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error rate : 17%


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I. M.Experiments· Australian credit data

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error rate : 13.9%Huang et al.(2005) error rate: 15%


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I. M.Conclusions· This paper presented a clustering algorithm for

subspace clustering for mixed numeric and categorical data.

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I. M.Comments· Advantage

· Applications Subspace clustering.

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Documents

Presenter : Keng -Yu Lin Author : Amir Ahmad , Lipika Dey PRL . 2011