density-based clustering

Clustering is one of the main tasks in data mining, which means grouping similar samples. In general, there is a wide variety of clustering algorithms. One of these categories is density-based clustering. Various algorithms have been proposed for this method; one of the most widely used algorithms called DBSCAN. DBSCAN can identify clusters of different shapes in the dataset and automatically identify the number of clusters. There are advantages and disadvantages in this algorithm. It is difficult to determine the input parameters of this algorithm by the user. Also, this algorithm is unable to detect clusters with different densities in the data set. ISB-DBSCAN algorithm is another example of density-based algorithms that eliminates the disadvantages of the DBSCAN algorithm. ISB-DBSCAN algorithm reduces the input parameters of DBSCAN algorithm and uses an input parameter k as the nearest neighbor's number. This method is also able to identify different density clusters, but according to the definition of the new core point, It is not able to identify some clusters in a different data set.
This paper presents a method for improving ISB-DBSCAN algorithm. A proposed approach, such as ISB-DBSCAN, uses an input parameter k as the number of nearest neighbors and provides a new definition for core point. This method performs clustering in three steps, with the difference that, unlike ISB-DBSCAN algorithm, it can create a new cluster in the final stage. In the proposed method, a new criterion, such as the number of dataset dimensions used to detect noise in the used data set. Since the determination of the k parameter in the proposed method may be difficult for the user, a new method with genetic algorithm is also proposed for the automatic estimation of the k parameter. To evaluate the proposed methods, tests were carried out on 11 standard data sets and the accuracy of clustering in the methods was evaluated. The results showe that the proposed method is able to achieve better results in different data sets compare to other available methods. In the proposed method, the automatic determination of k parameter also obtained acceptable results.

Clustering is one of the most important field of data mining that aims to divide data into meaningful subsets which are called clusters. This technique involves the process of finding natural groupings in the data set based on the similarities and di similarities which a little or no information about data are available. Over the decades, many clustering algorithms are created in different approaches or a combination of them. In this paper, an algorithm based on density and hierarchical approaches is presented. DBSCAN is one of the algorithms presented in the density-based approach. This algorithm requires two parameters that its determination is a great challenge. In the proposed method, DBSCAN algorithm parameters can be set without user involvement, so that potential clusters are found automatically. The clusters which are so close to each other are merged together until the quality of the final clusters to be enhanced properly. Thus, clusters could be more accurate and high quality. Finally, in order to test the new proposed algorithm, the real dataset in the UCI machine learning repository was used. The results indicate that the new algorithm is more efficient and accurate, and its speed is better than previous methods.