A Complementary Optimization Procedure for Final Cluster Analysis of Clustering Categorical Data

Seman, Ali; Sapawi, Azizian Mohd

doi:10.1007/978-3-030-33585-4_30

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…At present, no concrete solution has been obtained. For k-AMH algorithm, a recent attempt for the repeated k-AMH has been proposed [29]. This finding is quite promising; however, it must be noted that an optimal objective function is not necessarily produced optimal clustering results.…”

Section: Introductionmentioning

confidence: 99%

An Optimal and Stable Algorithm for Clustering Numerical Data

Seman

Sapawi

2021

Algorithms

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In the conventional k-means framework, seeding is the first step toward optimization before the objects are clustered. In random seeding, two main issues arise: the clustering results may be less than optimal and different clustering results may be obtained for every run. In real-world applications, optimal and stable clustering is highly desirable. This report introduces a new clustering algorithm called the zero k-approximate modal haplotype (Zk-AMH) algorithm that uses a simple and novel seeding mechanism known as zero-point multidimensional spaces. The Zk-AMH provides cluster optimality and stability, therefore resolving the aforementioned issues. Notably, the Zk-AMH algorithm yielded identical mean scores to maximum, and minimum scores in 100 runs, producing zero standard deviation to show its stability. Additionally, when the Zk-AMH algorithm was applied to eight datasets, it achieved the highest mean scores for four datasets, produced an approximately equal score for one dataset, and yielded marginally lower scores for the other three datasets. With its optimality and stability, the Zk-AMH algorithm could be a suitable alternative for developing future clustering tools.

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Section: Introductionmentioning

confidence: 99%

An Optimal and Stable Algorithm for Clustering Numerical Data

Seman

Sapawi

2021

Algorithms

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“…Then, cluster analysis is applied to study clusters' meaning and hidden insights based on data characteristics. Numerous cluster exploration techniques are available in which different types of clusters are generated by each of these techniques (Joseph et al, 2018;Seman & Sapawi, 2020).…”

Section: Introductionmentioning

confidence: 99%

An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets

Yusof

Ahmad

Zulkifli

et al. 2021

Journal of Information and Communication Technology

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The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. The Kohonen Self-Organising Map (KSOM) is one of the well-known clustering algorithms that can solve various problems without a pre-defined number of clusters. However, similar to other clustering algorithms, this algorithm requires sufficient data for its unsupervised learning process. The inadequate amount of class label data in a dataset significantly affects the clustering learning process, leading to inefficient and unreliable results. Numerous research have been conducted by hybridising and optimising the KSOM algorithm with various optimisation techniques. Unfortunately, the problems are still unsolved, especially separation boundary and overlapping clusters. Therefore, this research proposed an improved pheromone- based PKSOM algorithm known as iPKSOM to solve the mentioned problem. Six different datasets, i.e. Iris, Seed, Glass, Titanic, WDBC, and Tropical Wood datasets were chosen to investigate the effectiveness of the iPKSOM algorithm. All datasets were observed and compared with the original KSOM results. This modification significantly impacted the clustering process by improving and refining the scatteredness of clustering data and reducing overlapping clusters. Therefore, this proposed algorithm can be implemented in clustering other complex datasets, such as high dimensional and streaming data.

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A Complementary Optimization Procedure for Final Cluster Analysis of Clustering Categorical Data

Cited by 2 publications

References 13 publications

An Optimal and Stable Algorithm for Clustering Numerical Data

An Optimal and Stable Algorithm for Clustering Numerical Data

An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets

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