Firefly Algorithm with Mini Batch K-Means Entropy Measure for Clustering Heterogeneous Categorical Timber Data

Clustering analysis is the process of identifying similar patterns in various types of data. Heterogeneous categorical data consists of data on ordinal, nominal, binary, and Likert scales. The clustering solution for heterogeneous data clustering remains difficult due to partitioning complex and dis...

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Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 13; no. 7
Main Authors: Mahfuz, Nurshazwani Muhamad, Yusoff, Marina, Nordin, Muhammad Shaiful, Ahmad, Zakiah
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2022
Subjects:
Algorithms
Cluster analysis
Clustering
Constraint modelling
Entropy
Heuristic methods
Homogeneity
Optimization
ISSN:2158-107X, 2156-5570
Online Access:Get full text
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Summary:Clustering analysis is the process of identifying similar patterns in various types of data. Heterogeneous categorical data consists of data on ordinal, nominal, binary, and Likert scales. The clustering solution for heterogeneous data clustering remains difficult due to partitioning complex and dissimilarity features. It is necessary to find a solution to high-quality clustering techniques to efficiently determine the significant features of the data. This paper emphasizes using the firefly algorithm to reduce the distance gap between features and improve clustering performance. To obtain an optimal global solution for clustering, we proposed a hybrid of mini-batch k-means (MBK) clustering-based entropy distance measures (EM) with a firefly optimization algorithm (FA). This study compares the performance of hybrid K-Means, Agglomerative, DBSCAN, and Affinity clustering models with EM and FA. The evaluation uses a variety of data from the timber perception survey dataset. In terms of performance, the proposed MBK+EM+FA has superior and most effective clustering. It achieves a higher accuracy of 96.3 percent, a 97 percent F-measure, a 98 percent precision, and a 97 percent recall. Other external assessments revealed that the Homogeneity (HOMO) is 79.14 percent, the Fowlkes-Mallows Index (FMI) is 93.07 percent, the Completeness (COMP) is 78.04 percent, and the V-Measure (VM) is 78.58 percent. Both proposed MBK+EM+FA and MBK+EM took about 0.45s and 0.35s to compute, respectively. The excellent quality of the clustering results does not justify such time constraints. Surprisingly, the proposed model reduced the distance measure of all heterogeneous features. The future model could put heterogeneous categorical data from a different domain to the test.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2022.0130756