l-DBSCAN : A Fast Hybrid Density Based Clustering Method

Density based clustering techniques like DBSCAN can find arbitrary shaped clusters along with noisy outliers. A severe drawback of the method is its huge time requirement which makes it a unsuitable one for large data sets. One solution is to apply DBSCAN using only a few selected prototypes. But because of this the clustering result can deviate from that which uses the full data set. A novel method proposed in the paper is to use two types of prototypes, one at a coarser level meant to reduce the time requirement, and the other at a finer level meant to reduce the deviation of the result. Prototypes are derived using leaders clustering method. The proposed hybrid clustering method called l-DBSCAN is analyzed and experimentally compared with DBSCAN which shows that it could be a suitable one for large data sets.

Density based clustering techniques like DBSCAN can find arbitrary shaped clusters along with noisy outliers. A severe drawback of the method is its huge time requirement which makes it a unsuitable one for large data sets. One solution is to apply DBSCAN using only a few selected prototypes. But because of this the clustering result can deviate from that which uses the full data set. A novel method proposed in the paper is to use two types of prototypes, one at a coarser level meant to reduce the time requirement, and the other at a finer level meant to reduce the deviation of the result. Prototypes are derived using leaders clustering method. The proposed hybrid clustering method called l-DBSCAN is analyzed and experimentally compared with DBSCAN which shows that it could be a suitable one for large data sets.

Density based clustering techniques like DBSCAN are attractive because it can find arbitrary shaped clusters along with noisy outliers. Its time requirement is Oðn 2 Þ where n is the size of the dataset, and because of this it is not a suitable one to work with large datasets. A solution proposed in the paper is to apply the leaders clustering method first to derive the prototypes called leaders from the dataset which along with prototypes preserves the density information also, then to use these leaders to derive the density based clusters. The proposed hybrid clustering technique called rough-DBSCAN has a time complexity of OðnÞ only and is analyzed using rough set theory. Experimental studies are done using both synthetic and real world datasets to compare rough-DBSCAN with DBSCAN. It is shown that for large datasets rough-DBSCAN can find a similar clustering as found by the DBSCAN, but is consistently faster than DBSCAN. Also some properties of the leaders as prototypes are formally established.

2017

Density based clustering is an emerging field of data mining now a days. There is a need to enhance Research based on clustering approach of data mining. There are number of approaches has been proposed by various author. VDBSCAN, FDBSCAN, DD_DBSCAN, and IDBSCAN are the popular methodology. These approaches are use to ignore the information regarding attributes of an objects. This paper is collection of various information of density based clustering. It also throws some light on the DBSCAN.

Clustering is one of the data mining techniques that extracts knowledge from spatial datasets. DBSCAN algorithm was considered as well-founded algorithm as it discovers clusters in different shapes and handles noise effectively. There are several algorithms that improve DBSCAN as fast hybrid density algorithm (L-DBSCAN) and fast density-based clustering algorithm. In this paper, an enhanced algorithm is proposed that improves fast density-based clustering algorithm in the ability to discover clusters with different densities and clustering large datasets.

Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely used in many areas of science due to its simplicity and the ability to detect clusters of different sizes and shapes. However, the algorithm becomes unstable when detecting border objects of adjacent clusters as was mentioned in the article that introduced the algorithm. The final clustering result obtained from DBSCAN depends on the order in which objects are processed in the course of the algorithm run. In this article, a modified version of the DBSCAN algorithm is proposed to solve this problem. It was shown that by using the revised algorithm the clustering results are considerably improved, in particular for data sets containing dense structures with connected clusters.

International Journal of Machine Learning and Computing, 2013

Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. The traditional algorithms do not meet the latest multiple requirements simultaneously for objects. Density-based clustering algorithms find clusters based on density of data points in a region. DBSCAN algorithm is one of the density-based clustering algorithms. It can discover clusters with arbitrary shapes and only requires two input parameters.In this paper, we propose a new algorithm based on DBSCAN. We design a new method for automatic parameters generation that create clusters with different densities and generates arbitrary shaped clusters. The kd-tree is used for increasing the memory efficiency. The performance of proposed algorithm is compared with DBSCAN. Experimental results indicate the superiority of proposed algorithm.

International Journal of Computer Applications, 2010

The DBSCAN [1] algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. As the original DBSCAN algorithm uses the distance measures to compute the distance between objects, it consumes so much processing time and its computation complexity comes as O (N 2). In this paper we have proposed a new algorithm to improve the performance of DBSCAN algorithm. The existing algorithms A Fast DBSCAN Algorithm[6] and Memory effect in DBSCAN algorithm[7] has been combined in the new solution to speed up the performance as well as improve the quality of the output. As the RegionQuery operation takes long time to process the objects, only few objects are considered for the expansion and the remaining missed border objects are handled differently during the cluster expansion. Eventually the performance analysis and the cluster output show that the proposed solution is better to the existing algorithms.

Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Cluster analysis is a primary method for database mining [8]. Among different types of cluster the density cluster has advantages as its clusters are easy to understand and it does not limit itself to shapes of clusters. But existing density-based algorithms are lagging behind. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-data sets there does not even exist a global parameter setting for which the result of the clustering algorithm describes the intrinsic clustering structure accurately [1][2]. This paper gives a survey of density based clustering algorithms. DBSCAN [15] is a base algorithm for density based clustering techniques. It can detect the clusters of different shapes and sizes from large amount of data which contains noise and outliers. The main drawback of traditional clustering algorithm was largely recovered by VDBSCAN algorithm. But in VDBSCAN algorithm the value of parameter ‘K’ was a user input dependent parameter. It largely degrades the efficiency of permanent Eps. In our proposed method the Eps is determined by the value of ‘k’ in varied density based spatial cluster analysis by declaring ‘k’ as variable one by using algorithmic average determination and distance measurement by Cartesian method and Cartesian product on two dimensional spatial dataset where data are sparsely distributed. So the objective is to enhance the existing DBSCAN algorithm by automatically selecting the input parameters and to find the density varied clusters. The proposed algorithm discovers arbitrary shaped clusters, requires no input parameters and uses the same definitions of DBSCAN algorithm.

International Journal of Computer Applications, 2011

This paper presents a comparative study of three Density based Clustering Algorithms that are DENCLUE, DBCLASD and DBSCAN. Six parameters are considered for their comparison. Result is supported by firm experimental evaluation. This analysis helps in finding the appropriate density based clustering algorithm in variant situations.