View
61
Download
0
Category
Preview:
DESCRIPTION
A k-mean clustering algorithm for mixed numeric and categorical data. Presenter : Shao -Wei Cheng Authors : Amir Ahmad, Lipika Dey. DKE 2007. Outline. Motivation Objective Methodology Experiments Conclusion Comments. Motivation. - PowerPoint PPT Presentation
Citation preview
Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
A k-mean clustering algorithm for mixed numeric and categorical data
Presenter : Shao-Wei ChengAuthors : Amir Ahmad, Lipika Dey
DKE 2007
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
2
Outline
Motivation Objective Methodology Experiments Conclusion Comments
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Motivation
3
The traditional k-mean algorithm is limited to numeric data. The Huang’s cost algorithm tried to cluster mixed numeric
and categorical data
The cluster center is represented by the mode of the cluster. Use the binary distance between two categorical attribute values. The significance(weight) of numeric attribute is taken to be 1, and γj is
a user-defined parameter.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
4
Objectives
This paper attempts to alleviate the short-comings of Huang’s cost algorithm. Propose a new representation for the cluster center. Computing distance between two categorical values by the overall
distribution of categorical attribute. The parameter is defined by the contribution of a categorical
attribute.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Cost function
The Huang’s cost algorithm
The proposed cost algorithm
5
Methodology
The distance between De Niro and Stewart is ?
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
6
Methodology
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
7
Methodology
Significance of numeric attribute
The numeric attributes need to be discretized. equal width discretization
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
8
Methodology
Algorithm① Initialization.
② Computing the cluster centers.
③ Assign the data element to the cluster whose center is closest to it
④ Repeat 2 and 3, until clusters do not change or for a fixed number of iterations.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Evaluation method
Data sets Iris – all numeric attributes Vote – all categorical attributes Heart disease data – mixed data set Australian credit data – mixed data set
Experiments
9
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
10
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Conclusion
11
This paper introduced a new distance measure for categorical attribute values and proposed a modified k-mean algorithm for clustering mixed data sets.
The results obtained with this algorithm over a number of real-world data sets are highly encouraging.
Future work Other methods for discretizing numeric valued attributes. Other implementations of k-mean algorithm.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
12
Comments
Advantage The view of overall attributes is good.
Drawback …
Application Mixed data sets clustering.
Recommended