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

作     者：Mahfuz, Nurshazwani Muhamad Yusoff, Marina Nordin, Muhammad Shaiful Ahmad, Zakiah 

作者机构：Univ Teknol MARA Fac Comp & Math Sci Shah Alam Malaysia Math Sci Univ Teknol MARA Inst Big Data Analyt & Artificial Intelligence Fac Comp & Math Sci Shah Alam Malaysia Malaysian Timber Ind Board Menara PGRM Jalan Pudu Ulu Kuala Lumpur 56100 Malaysia Univ Teknol MARA Coll Engn Shah Alam Malaysia 

出 版 物：《INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS》 (Intl. J. Adv. Comput. Sci. Appl.)

年 卷 期：2022年第13卷第7期

页      面：461-468页

核心收录：

基　　金：Ministry of Higher Education (Fundamental Research Grant Scheme (FRGS)) [600-IRMI/FRGS 5/3 (213/2019)] Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA and Malaysian Timber Industry Board, Malaysia 

主　　题：Clustering mini batch k-means entropy heterogeneous categorical firefly optimization algorithm 

摘      要：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.