Clustering technology has been applied in numerous applications. It can enhance the performance of information retrieval systems, it can also group Internet users to help improve the click-through rate of on-line advertising, etc. Over the past few decades, a great many data clustering algorithms have been developed, including K-Means, DBSCAN, Bi-Clustering and Spectral clustering, etc. In rece...

Several clustering algorithms have been proposed for class identification in spatial databases such as earth observation databases. The effectivity of the well-known algorithms such as DBSCAN, however, is somewhat limited because they do not fully exploit the richness of the different types of data contained in a spatial database. In this paper, we introduce the concept of density-connected set...

Finding clusters in data is a challenging problem especially when the clusters are being of widely varied shapes, sizes, and densities. Herein a new scalable clustering technique which addresses all these issues is proposed. In data mining, the purpose of data clustering is to identify useful patterns in the underlying dataset. Within the last several years, many clustering algorithms have been...

With the advent of Web 2.0, we see a new and differentiated scenario: there is more data than that can be effectively analyzed. Organizing this data has become one of the biggest problems in Computer Science. Many algorithms have been proposed for this purpose, highlighting those related to the Data Mining area, specifically the clustering algorithms. However, these algorithms are still a compu...

With the Popularity of shopping online in people's daily economic life, the commodity recommendation mechanism in e-commerce platform is presented to help customers quickly and accurately find the suitable product. Using the possibility of changing membrane structure, a variant of P system with active membranes is proposed to solve commodity recommendation problems. In this paper, the commodity...

The main purpose of this paper is to achieve improvement in thespeed of Fuzzy Joint Points (FJP) algorithm. Since FJP approach is a basisfor fuzzy neighborhood based clustering algorithms such as Noise-Robust FJP(NRFJP) and Fuzzy Neighborhood DBSCAN (FN-DBSCAN), improving FJPalgorithm would an important achievement in terms of these FJP-based meth-ods. Although FJP has many advantages such as r...

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 mod#ed 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 K...

This paper attempts to mine the hidden individual behavior pattern from the raw users’ trajectory data. Based on DBSCAN, a novel spatio-temporal data clustering algorithm named Speed-based Clustering Algorithm was put forward to find slow-speed subtrajectories (i.e., stops) of the single trajectory that the user stopped for a longer time. The algorithm used maximal speed and minimal stopping ti...

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...