on data labeling for clustering categorical data
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Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
On Data Labeling for Clustering Categorical Data
Hung-Leng Chen, Kun-Ta Chuang, Member, and Ming-Syan Chen
TKDE, Vol. 19, No. 11, 2008, pp. 1458-1471.
Presenter : Wei-Shen Tai
2008/11/4
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Outline Introduction Related work Model of MARDL (MAximal Resemblance Data
Labeling) Experimental results Conclusions Comments
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Motivation Sampling
Scales down the size of the database and speed up clustering algorithms.
Problem comes from how to allocate the unclustered data into appropriate clusters.
LargeDatabase
Sampled data
SamplingClustering
Unclustered data
Labeling ?
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Objective Data Labeling
Gives each unclustered data point the most appropriate cluster label.
MARDL is independent of clustering algorithms, and any categorical clustering algorithm can be utilized in this framework.
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Categorical cluster representative Node
Attribute name + attribute value. E.g. [A1=a], [A2=m] is an node. N-nodeset
A set of n nodes, in which every node is a member of the distinct attribute Aa. E.g. {[A1=a], [A2=m]} is a 2-nodeset.
Independent nodesets Two nodesets do not contain nodes from the same attributes are said to
be independent with each other in a represented cluster. E.g. {[A1=a], [A2=m]} and {[A3=c]} p({[A1=a], [A2=m],[A3=c]}) =
p({[A1=a], [A2=m]})*p({[A3=c]})
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Node and n-nodeset importance Information theorem
Entropy
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N-nodeset importance representative(NNIR)
NNIR tree constructing and pruning An Apriori-like algorithm.
Initialization Computing candidate nodeset
importance and pruning Generating candidate nodeset
Pruning Threshold
Importance of t nodeset is less than a predefined θ.
Relative maximum Importance of (t+1) nodeset is
larger than importance of t nodeset. Hybrid
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Maximal resemblance data labeling
Goal of MARDL Decide the most appropriate cluster label ci for the
unlabeled data point.
A unclustered data point {[A1=a], [A2=m],[A3=c ]} to the combination{[A1=a], [A2=m]} and {[A3=c ]} in Cluster c1.
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Approximate algorithm for MARDL Only one combination is considered and utilized
Tree nodes are queued and sorted by importance value. The nodeset with maximal importance is selected. Those nodesets which are not independent with the
selected nodeset are removed from the queue. A unclustered data point
{[A1=a], [A2=m],[A3=c ]}and a tree nodeset queue.
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Experimental results
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Conclusions MARDL
Allocates unlabeled data point into appropriate clusters when the sampling technique is utilized to cluster a very large categorical database.
NIR A categorical cluster representative technique.
NNIR A more powerful representative than NIR while the
combinations of attribute values are considered.
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Comments Advantage
A good method to assign unclustered data to appropriate trained clusters in categorical data sampling clustering methods.
The concept, derived from existed method (Apriori and information theorem) , is easy to understand and accept.
MARDL is independ of clustering methods and any categorical clustering algorithm can be utilized in this framework.
Drawback It spends much time to construct the tree of each cluster and the tree is quite complex to
represent cluster. Because the importance of t+1 nodeset may be larger than the importance of t nodeset, it
will take much time to process the hybrid pruning in computing all of candidate t+1 nodeset.
Application Unclustered data classification while the sampling technique is utilized to cluster a very
large categorical database.
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