Density-based clustering is a sort of clustering analysis methods, which can discover clusters with arbitrary shape and is insensitive to noise data. The efficiency of data mining algorithms is strongly needed with data becoming larger and larger. In this paper, we present a new fast clustering algorithm called CURD, which means Clustering Using References and Density. Its creativity is capturi...

With increasing complexity of manufacturing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume make any kind of manual data analysis infeasable. At this point, data mining techniques become interesting. The application of current techniques is of complex nature because most of the data is captured by sensor measurement tools. Therefore, ever...

In this paper, an objective function based on minimal spanning tree (MST) of data points is proposed for clustering and a density-based clustering technique has been used in an attempt to optimize the specified objective function in order to detect the ―natural grouping‖ present in a given data set. A threshold based on MST of data points of each cluster thus found is used to remove noise (if a...

With the advent of commercial depth sensors, such as the Kinect, new opportunities to develop applications and interactive systems that use the human body are created. However, those sensors capture a large amount of 3D data, known as point clouds, that have to be processed, trying to obtain as much relevant information as possible. Clustering algorithms, normally used in data mining, are usefu...

Article history: Received 1 September 2012 Received in revised form 5 May 2014 Accepted 24 November 2014 Available online 29 November 2014 The basic DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm uses minimum number of input parameters, very effective to cluster large spatial databases but involves more computational complexity. The present paper proposes a new s...

The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lun...

Representative-based clustering algorithms are quite popular due to their relative high speed and because of their sound theoretical foundation. On the other hand, the clusters they can obtain are limited to convex shapes and clustering results are also highly sensitive to initializations. In this paper, a novel agglomerative clustering algorithm called MOSAIC is proposed which greedily merges ...

With the increasing use of mobile GPS (global positioning system) devices, a large volume of trajectory data on users can be produced. In most existing work, trajectories are usually divided into a set of stops and moves. In trajectories, stops represent the most important and meaningful part of the trajectory; there are many data mining methods to extract these locations. DBSCAN (density-based...

Content-Centric Networks (CCNs) have recently emerged as an innovative trend to overcome many inherent security problems in the IP-based (host-based) networks by securing the content itself rather than the channel through which it travels. In this network architecture new kinds of attacks -ranging from DoS to privacy attackswill appear. Therefore, it is becoming necessary to design a flexible a...