ABSTRACT:
Clustering of data with high dimension and variable densities poses a
remarkable challenge to the traditional density-based clustering methods.
Recently, entropy, a numerical measure of the uncertainty of information, can
be used to measure the border degree of samples in data space and also select
significant features in feature set. It was used in our new framework based on
the sparsity-density entropy (SDE) to cluster the
data with high dimension and variable densities. First, SDE conducts
high-quality sampling for multidimensional data and selects the representative
features using sparsity score entropy (SSE). Second,
the clustering results and noises are obtained adopting a new density-variable
clustering method called density entropy (DE). DE automatically determines the
border set based on the global minimum of border degrees and then adaptively
performs cluster analysis for each local cluster based on the local minimum of
border degrees. The effectiveness and efficiency of the proposed SDE framework
are validated on synthetic and real data sets in comparison with several
clustering algorithms. The results showed that the proposed SDE framework
concurrently detected the noises and processed the data with high dimension and
various densities.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
·
System : Pentium Dual Core.
·
Hard Disk : 120 GB.
·
Monitor :
15’’ LED
·
Input
Devices : Keyboard, Mouse
·
Ram : 1
GB
SOFTWARE
REQUIREMENTS:
·
Operating
system : Windows 7.
·
Coding
Language : JAVA/J2EE
·
Tool : Netbeans 7.2.1
·
Database
: MYSQL
REFERENCE:
Sheng Li, Member, IEEE, Lusi Li, Student Member, IEEE, Jun Yan, Member, IEEE, and Haibo He, Fellow, IEEE, “SDE: A Novel Clustering Framework
Based on Sparsity-Density Entropy”, IEEE Transactions
on Knowledge and Data Engineering, 2018.