Many clustering algorithms suffer from scalability problems on massive datasets and do not support any user interaction during runtime. To tackle these problems, anytime clustering algorithms are proposed. They produce a fast approximate result which is continuously refined during the further run. Also, they can be stopped or suspended anytime and provide an answer. In this paper, we propose a ...

We present a new result concerning the parallelisation of DBSCAN, a Data Mining algorithm for density-based spatial clustering. The overall structure of DBSCAN has been mapped to a skeletonstructured program that performs parallel exploration of each cluster. The approach is useful to improve performance on high-dimensional data, and is general w.r.t. the spatial index structure used. We report...

Corresponding Author: Muhammad Arif Saifudin Satellite Technology Center, Indonesian National Institute of Aeronautics and Space, Bogor, Indonesia Email: [email protected] Abstract: A new method to generate star catalog using density-based clustering is proposed. It identifies regions of a high star density by using Density-Based Spatial Clustering of Application with Noise (DBSCAN) alg...

In this paper we propose a new density based clustering algorithm via using the Mahalanobis metric. This is motivated by the current state-of-the-art density clustering algorithm DBSCAN and some fuzzy clustering algorithms. There are two novelties for the proposed algorithm: One is to adopt the Mahalanobis metric as distance measurement instead of the Euclidean distance in DBSCAN and the other ...

Malware Classification has been a challenging problem in the recent past and several researchers have attempted to solve this problem using various tools. It is security threat which can break machine operation while not knowing user’s data and it's tough to spot its behavior. This paper proposes a novel technique using DBSCAN (Density based Kmeans) algorithmic rule to spot the behavior of malw...

Tibetan text clustering has potential in Tibetan information processing domain. In this paper, clustering research across Chinese and Tibetan texts is proposed to benefit Chinese and Tibetan machine translation and sentence alignment. A Tibetan and Chinese keyword table is the main way to implement the text clustering across these two languages. Improved Kmeans and improved density-based spatia...

The problem of detecting clusters of points belonging to a spatial point process arises in many applications. In this paper , we introduce the new clustering algorithm DBCLASD (Distribution Based Clustering of LArge Spatial Databases) to discover clusters of this type. The results of experiments demonstrate that DBCLASD, contrary to partitioning algorithms such as CLARANS, discovers clusters of...

In this work we introduce an anisotropic density-based clustering algorithm. It outperforms DBSCAN and OPTICS for the detection of anisotropic spatial point patterns and performs equally well in cases that do not explicitly benefit from an anisotropic perspective. ADCN has the same time complexity as DBSCAN and OPTICS, namely O(n log n) when using a spatial index, O(n2) otherwise. STKO@Geograph...

As a kind of stream data mining method, stream clustering has great potentiality in areas such as network traffic analysis, intrusion detection, etc. This paper proposes a novel grid-based clustering algorithm for stream data, which has both advantages of grid mapping and DBSCAN algorithm. The algorithm adopts the two-phase model and in the online phase, it maps stream data into a grid and the ...