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Momotaz Begum
Dhaka University of Engineering and Technology

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Develop a dynamic DBSCAN algorithm for solving initial parameter selection problem of the DBSCAN algorithm Md. Zakir Hossain; Md. Jakirul Islam; Md. Waliur Rahman Miah; Jahid Hasan Rony; Momotaz Begum
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1602-1610

Abstract

The amount of data has been increasing exponentially in every sector such as banking securities, healthcare, education, manufacturing, consumer-trade, transportation, and energy. Most of these data are noise, different in shapes, and outliers. In such cases, it is challenging to find the desired data clusters using conventional clustering algorithms. DBSCAN is a popular clustering algorithm which is widely used for noisy, arbitrary shape, and outlier data. However, its performance highly depends on the proper selection of cluster radius (Eps) and the minimum number of points (MinPts) that are required for forming clusters for the given dataset. In the case of real-world clustering problems, it is a difficult task to select the exact value of Eps and (MinPts) to perform the clustering on unknown datasets. To address these, this paper proposes a dynamic DBSCAN algorithm that calculates the suitable value for (Eps) and (MinPts) dynamically by which the clustering quality of the given problem will be increased. This paper evaluates the performance of the dynamic DBSCAN algorithm over seven challenging datasets. The experimental results confirm the effectiveness of the dynamic DBSCAN algorithm over the well-known clustering algorithms.
An improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets Momotaz Begum; Bimal Chandra Das; Md. Zakir Hossain; Antu Saha; Khaleda Akther Papry
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp600-610

Abstract

Manipulating high-dimensional data is a major research challenge in the field of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen self-organizing map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modified KSOM in terms of predictive performance with topographic and quantization error.
Co-Authors Antu Saha Bimal Chandra Das Jahid Hasan Rony Khaleda Akther Papry Md. Jakirul Islam Md. Waliur Rahman Miah Md. Zakir Hossain