Abstract Details
Activity Number:
|
318
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #308950 |
Title:
|
Distinctness Evaluation of Unknown Clustering Structure
|
Author(s):
|
Ewa Nowakowska*+
|
Companies:
|
Institute of Computer Science, PAS
|
Keywords:
|
multivariate normal mixture ;
principal component analysis ;
clustering
|
Abstract:
|
There are numerous measures designed to evaluate quality of a given data grouping, however they all require the partition to be already determined. The objective of this paper is to present a method of preliminary analysis that operates on raw data only and evaluates the distinctness of the intrinsic yet undetermined clustering structure in data (clusterability). In the framework of heterogeneous mixture of multivariate normal distributions, we first introduce a data transformation that preserves initial distinctness of the unknown clustering structure up to a negligible error. It is designed to bring the subspace of largest overall variability close to the subspace of best between cluster separation, hence allowing for efficient dimension reduction without the knowledge of clusters. Then, in the subspace of reduced dimension we propose the clusterability coefficient and show the results of its performance assessment. The coefficient measures to what extent the data may support meaningful and efficient clustering. Among others, the information may further be used in feature selection tasks.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.