Density-based method has emerged as a worthwhile class for clustering data streams. Recently, a number of density-based algorithms have been developed for clustering data streams. However, existing density-based data stream clustering algorithms are not without problem. There is a dramatic decrease in the quality of clustering when there is a range in density of data. In this paper, a new metho...

Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will o...

In this paper, we have proposed, developed and experimentally validated our novel subspace data stream clustering, termed PreDeConStream. The technique is based on the two phase mode of mining streaming data, in which the first phase represents the process of the online maintenance of a data structure, that is then passed to an offline phase of generating the final clustering model. The techniq...

Color image segmentation is an important but still open problem in image processing. In this paper, we propose a method for this problem by integrating the spatial connectivity and color feature of the pixels. Considering that an image can be regarded as a dataset in which each pixel has a spatial location and a color value, color image segmentation can be obtained by clustering these pixels in...

Many real-world data sets, like data from social media or bibliographic data, can be represented as heterogeneous networks with several vertex types. Often additional attributes are available for the vertices, such as keywords for a paper. Clustering vertices in such networks, and analyzing the complex interactions between clusters of different types, can provide useful insights into the struct...

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). In this paper we have proposed a new algorithm to improv...

As of 1996, when a special issue on density-based clustering was published (DBSCAN) (Ester et al., 1996), existing clustering techniques focused on two categories: partitioning methods, and hierarchical methods. Partitioning clustering attempts to break a data set into K clusters such that the partition optimizes a given criterion. Besides difficulty in choosing the proper parameter K, and inca...

Clustering data into meaningful groups is one of most important tasks of both artificial intelligence and data mining. In general, clustering methods are considered unsupervised. However, in recent years, so-named constraints become more popular as means of incorporating additional knowledge into clustering algorithms. Over the last years, a number of clustering algorithms employing different t...

Clustering is an important data exploration task. Its use in data mining is growing very fast. Traditional clustering algorithms which no longer cater to the data mining requirements are modified increasingly. Clustering algorithms are numerous which can be divided in several categories. Two prominent categories are distance-based and density-based (e.g. K-means and DBSCAN, respectively). While...