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Publication
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| Title of Article |
Efficient algorithms based on the k-means and Chaotic League Championship Algorithm for numeric, categorical, and mixed-type data clustering |
| Date of Acceptance |
30 December 2017 |
| Journal |
| Title of Journal |
Expert Systems with Applications |
| Standard |
ISI |
| Institute of Journal |
Expert Systems with Applications |
| ISBN/ISSN |
0957-4174 |
| Volume |
2017 |
| Issue |
90 |
| Month |
December |
| Year of Publication |
2017 |
| Page |
146-167 |
| Abstract |
The success rates of the expert or intelligent systems depend on the selection of the correct data clusters.
The k-means algorithm is a well-known method in solving data clustering problems. It suffers not only
from a high dependency on the algorithm’s initial solution but also from the used distance function. A
number of algorithms have been proposed to address the centroid initialization problem, but the produced solution does not produce optimum clusters. This paper proposes three algorithms (i) the search
algorithm C-LCA that is an improved League Championship Algorithm (LCA), (ii) a search clustering using
C-LCA (SC-LCA), and (iii) a hybrid-clustering algorithm called the hybrid of k-means and Chaotic League
Championship Algorithm (KSC-LCA) and this algorithm has of two computation stages. The C-LCA employs chaotic adaptation for the retreat and approach parameters, rather than constants, which can enhance the search capability. Furthermore, to overcome the limitation of the original k-means algorithm
using the Euclidean distance that cannot handle the categorical attribute type properly, we adopt the
Gower distance and the mechanism for handling a discrete value requirement of the categorical value attribute. The proposed algorithms can handle not only the pure numeric data but also the mixed-type data
and can find the best centroids containing categorical values. Experiments were conducted on 14 datasets
from the UCI repository. The SC-LCA and KSC-LCA competed with 16 established algorithms including the
k-means, k-means++, global k-means algorithms, four search clustering algorithms and nine hybrids of
k-means algorithm with several state-of-the-art evolutionary algorithms. The experimental results show
that the SC-LCA produces the cluster with the highest F-Measure on the pure categorical dataset and the
KSC-LCA produces the cluster with the highest F-Measure for the pure numeric and mixed-type tested
datasets. Out of 14 datasets, there were 13 centroids produced by the SC-LCA that had better F-Measures
than that of the k-means algorithm. On the Tic-Tac-Toe dataset containing only categorical attributes,
the SC-LCA can achieve an F-Measure of 66.61 that is 21.74 points over that of the k-means algorithm
(44.87). The KSC-LCA produced better centroids than k-means algorithm in all 14 datasets; the maximum
F-Measure improvement was 11.59 points. However, in terms of the computational time, the SC-LCA and
KSC-LCA took more NFEs than the k-means and its variants but the KSC-LCA ranks first and SC-LCA ranks
fourth among the hybrid clustering and the search clustering algorithms that we tested. Therefore, the
SC-LCA and KSC-LCA are general and effective clustering algorithms that could be used when an expert
or intelligent system requires an accurate high-speed cluster selection. |
| Keyword |
Data clustering, Search clustering algorithm, Hybrid clustering algorithm, League Championship Algorithm (LCA), Chaos optimization algorithms (COA), Mixed-type data |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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