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Md. Zakir Hossain
Dhaka University of Engineering and Technology

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A dynamic K-means clustering for data mining Md. Zakir Hossain; Md.Nasim Akhtar; R.B. Ahmad; Mostafijur Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp521-526

Abstract

Data mining is the process of finding structure of data from large data sets. With this process, the decision makers can make a particular decision for further development of the real-world problems. Several data clusteringtechniques are used in data mining for finding a specific pattern of data. The K-means method isone of the familiar clustering techniques for clustering large data sets.  The K-means clustering method partitions the data set based on the assumption that the number of clusters are fixed.The main problem of this method is that if the number of clusters is to be chosen small then there is a higher probability of adding dissimilar items into the same group. On the other hand, if the number of clusters is chosen to be high, then there is a higher chance of adding similar items in the different groups. In this paper, we address this issue by proposing a new K-Means clustering algorithm. The proposed method performs data clustering dynamically. The proposed method initially calculates a threshold value as a centroid of K-Means and based on this value the number of clusters are formed. At each iteration of K-Means, if the Euclidian distance between two points is less than or equal to the threshold value, then these two data points will be in the same group. Otherwise, the proposed method will create a new cluster with the dissimilar data point. The results show that the proposed method outperforms the original K-Means method.
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.
Perspective and Challenge of Tidal Power in Bangladesh Md. Alamgir Hossain; Md. Zakir Hossain; Md. Mijanur Rahman; Md. Atiqur Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 11: November 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i11.pp7558-7563

Abstract

Tidal power can play a vital role in integrating as new source of renewable energy to the off-grid power connection in isolated areas, namely Sandwip, in Bangladesh. It can reduce the present energy crisis and improve the social, environmental and economical perspective of Bangladesh. Tidal energy is becoming popular around the world due to its own facilities. The development of any country largely depends on energy sector improvement. Lack of energy sector is because hampering of progress of any country development and energy sector will be stable by only depend on sustainable energy sources. Renewable energy is the only sustainable solution of secure energy which is environmental friendly. Bangladesh has a huge potential of tidal power at different locations but effective measure on this issue have not been considered sincerely. This paper summarizes the current energy scenario and Bangladesh can produce power approximately 53.19MW across the country to reduce the growing energy demand utilizing tidal energy as well as it is shown that Sandwip has high potential of producing tidal power which is approximately 16.49MW by investing only US $10.37 millions. Besides this, cost management for tidal power plant also has been discussed.
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. Alamgir Hossain Md. Atiqur Rahman Md. Jakirul Islam Md. Mijanur Rahman Md. Waliur Rahman Miah Md.Nasim Akhtar Momotaz Begum Mostafijur Rahman R.B. Ahmad