new link based approach for categorical data clustering

7/28/2019 New link based approach for categorical data clustering

1/17

NEW LINK BASED APPROACH

FOR CATEGORICAL DATACLUSTERING

By,

CHIRANTH B O4th Sem M.tech

KNOWLEDGE AND DATA ENGINEERING


2/17

July 22, 2013 2

Presentation Outline

Introduction to Clustering

Abstract

Existing System

Proposed System

Experimental DesignExperimental results

Conclusion


3/17

ClusteringIntroduction

ClusteringGrouping similar kind of data.

Data clustering concerns how togroup a set ofobjects based on their

similarity of attributes.

Main methods Partitioning: K-Means

Hierarchical : BIRCH,ROCK,

Density-based: DBSCAN,

A good clustering method will produce high quality clusters with

high intra-class similarity

low inter-class similarity

July 22, 2013 3


4/17

July 22, 2013 4

ABSTRACT

The categorical data clustering methods are generating

results based on incomplete information.

This problem degrades the quality of the clustering result.

This paper presents a new link-based approach for

categorical data clustering which improves results by

discovering unknown entries through similarity betweenclusters


5/17

Existing Methods

K-means cannot cluster the categorical data.

SQUEEZER and CACTUS generates final clustering

using incomplete information.

Many data entries are left unknown.

July 22, 2013 5


6/17

Proposed Methods

Link based approach improves the

matrix by discovering the unknown

entries.

An efficient link based algorithm used tofind similarity between clusters.

July 22, 2013 6


7/17

July 22, 2013 7

Introduction to NLCD

Designed for very large data sets:

Time and memory are limited

Only one scan of data is necessary

Does not need the whole data set in advance

Two key Modules:

Scans the database to build an Binary Matrix.Building refined matrix using Weighted Triple QualityAlgorithm.


8/17

Basic process

July 22, 2013 8

Dataset X

Clustering

1

Consensus

FunctionClustering

2

Clustering

M


9/17

July 22, 2013 9

Binary MatrixPairWise-Similarity Matrix

Clustering


10/17

Weighted Triple Quality

ALGORITHM - WTQ (G, , )

G = (V, W), a weighted graph, where , ;

, a set of adjacent neighbors of ;

= ;

, the WTQ measure of and;

0

For each c

Ifc

+1

Return

Following that, the similarity between clusters and can be estimated by

Sim , =

,

July 22, 2013 10


11/17

July 22, 2013 11

Over Lapping Member

Wx,y W where Cx ,Cy V

Cluster Network

wxy =

,


12/17

July 22, 2013 12

Experimental Results

Input parameters:

Memory (M): 5% of data set

Disk space (R): 20% ofM

Initial threshold (T): 0.0Page size (P): 1024 bytes


13/17

July 22, 2013 13

Experimental Results

KMEANS clustering

No Time D # Scan DS Time D # Scan

1 43.9 2.09 289 1o 33.8 1.97 197

2 13.2 4.43 51 2o 12.7 4.20 293 32.9 3.66 187 3o 36.0 4.35 241

No Time D # Scan DS Time D # Scan

1 11.5 1.87 2 1o 13.6 1.87 2

2 10.7 1.99 2 2o 12.1 1.99 2

3 11.4 3.95 2 3o 12.2 3.99 2

NLCD clustering


14/17

July 22, 2013 14

Conclusions

A New Link Based Clustering that stores the

clustering features in Matrix.

Given a limited amount of main memory, NLCD

can minimize the time required for I/O.

The problem of constructing the refined matrix is

efficiently resolved by similarity among

categorical clusters


15/17

Future Work

The first prominent future work includes an

extensive study regarding the behavior of other

link-based similarity measures within thisproblem context.

The second prominent future work is the new

method will be applied to specific domains,

including tourism and medical data sets.

July 22, 2013 15


16/17

References

IEEE Journal on Data Mining

http://ilpubs.stanford.edu:8090/508/1/2001-41.pdf

IEEE Journal on Knowledge and data engineering

http://en.wikipedia.org/wiki/Clustering_algorithm

July 22, 2013 16


17/17

Q&A

Thank you for your patience

July 22, 2013 17

new link based approach for categorical data clustering

Documents