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Catching the Trend- A Framework for Clustering Concept-Drifting Categorical Data. Hung- Leng Chen, Ming- Syan Chen, and Su-Chen Lin TKDE, Vol.21, No. 5, 2009, pp. 652-665. Presenter : Wei- Shen Tai 200 9 / 7/1. Outline. Introduction Preliminaries Node Importance Representative - PowerPoint PPT Presentation
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Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
Catching the Trend- A Framework for Clustering Concept-Drifting Categorical Data
Hung-Leng Chen, Ming-Syan Chen, and Su-Chen Lin
TKDE, Vol.21, No. 5, 2009, pp. 652-665.
Presenter : Wei-Shen Tai
2009/7/1
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Outline
Introduction Preliminaries
Node Importance Representative Drifting concept detection Clustering relationship analysis Experimental results Conclusion Comments
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Motivation
Find concept drifting with time in categorical domain. For example, the buying preferences of customers may change with
time.
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Objective
A framework for performing clustering on the categorical time-evolving data Detects concept drifting and analyzes relationship between drifting-
concepts.
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Node importance representative
NIR Represents a cluster as the distribution of the attribute
values, which are called “nodes” (e.g. [age = 50-59]). Importance of node Iir in cluster ci.
Similar to TFIDF and Entropy
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Drifting concept detection
DCD Detect the difference of cluster distributions between the
current subset St and the last clustering result C[te, t-1].
Data labeling
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Data labeling and outlier detection Resemblance of input and cluster can be directly obtained
by summing up the nodes’ importance in the NIR
P 7 C1, 0.029
= 0.5
P 7 C2, (0.5+0.029+1)=1.529
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Cluster distributions comparison
Clustering results are said to be different according to the following two criteria.1. If quite a large number of outliers are found.
2. If quite a large number of clusters are varied in the ratio of data points.
(0.4)
(0.5)
(0.3)
C1, |2/5 – 4/5| = 0.4
C2, |3/5 – 0/5| = 0.6Diff of results , 2/2 = 1
outlier, 1/5 = 0.2
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Clustering relationship analysis
CRA Explains the drifting concepts based on the evolving
clustering results. Node importance vector
Cluster distance using cosine measure
A,B,X,Y
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Experimental results
Scalability
Accuracy
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Discussion and conclusions
A framework to perform clustering on categorical time-evolving data. Detects the drifting concepts at different sliding
windows, Generates the clustering results based on the
current concept, Analyzes and shows the relationship between
clustering results by visualization.
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Comments Advantage
This proposed framework provides a solution for time-evolving data clustering in categorical domain.
It also provides an alternative for the similarity measurement between cluster and input in categorical data set based on NIR.
Drawback Merely categorical data can be processed in this framework with NIR, even
numerical data must be transformed to categorical labels as well. In other words, it seems unsuitable for clustering in mixed data domain.
The vector dimension of each class did not be reduced, it will spend too many spaces to preserve overall vector information.
Node important vector is similar to binary coding, it makes the result of cosine measurement be very tiny.
Application Concept-drifting detection for time-evolving data set in categorical domain.