Improving the Performance of the FCM Algorithm in Clustering using the DBSCAN Algorithm

Iranian Journal of Numerical Analysis and Optimization

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 05 مرداد 1402

نوع مقاله: 5NSCO-2023

شناسه دیجیتال (DOI): 10.22067/ijnao.2023.82361.1260

نویسندگان

Saede Barkhordari Firozabadi¹؛ Seyed Abolfazl Shahzadeh Fazeli^{* 1}؛ Jamal Zarepour Ahmadabadi¹؛ ‪Seyed Mehdi Karbassi²

¹Department of Computer Science, Faculty of Mathematical Sciences, Yazd University, Yazd, Iran.

²Department of Applied Mathematics, Faculty of Mathematical Sciences, Yazd University, Yazd, Iran.

چکیده

The $FCM$ algorithm is one of the most famous fuzzy clustering algorithms, but it gets stuck in local optima. In addition, this algorithm requires a number of clusters. Also, the $ DBSCAN $ algorithm, which is a density-based clustering algorithm, unlike the $ FCM $ algorithm should not be pre-numbered. If the clusters are specific and depend on the number of clusters, it can determine the number of clusters. Another advantage of the $ DBSCAN $ clustering algorithm over $FCM$ is its ability to cluster data of different shapes. In this paper, in order to overcome these limitations, a hybrid approach for clustering is proposed which uses $ FCM $ and $ DBSCAN $ algorithms. In this method, the optimal number of clusters and the optimal location for the centers of the clusters are determined based on the changes that take place according to the data set in three phases by predicting the possibility of the problems stated in the $FCM$ algorithm. With this improvement, the values of none of the initial parameters of $FCM$ algorithm are random, and in the first phase, it has been tried to replace these random values to the optimal in the $FCM$ algorithm, which has a significant effect on the convergence of the algorithm because it helps to reduce iterations. The proposed method has been examined on the Iris flower and compared the results with the basic $FCM$ algorithm and another algorithm. Results show the better performance of the proposed method.

کلیدواژه‌ها

Clustering؛ Fuzzy clustering؛ DBSCAN