Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning. They can deal with non-hyperspherical robust to outliers. However, the runtime of density-based is heavily dominated by neighborhood finding density estimation which time-consuming. Meanwhile, traditional acceleration methods using indexing techniques such as KD-tree may not...

The success of the Case Based Reasoning (CBR) system depends on the quality of the case data. This quality is dedicated to the study of the case base competence which is measured by the range of problems that can be satisfactorily solved. In fact, modeling case-base competence is a clamorous issue in the discipline of CBR. However, the existence of erroneous cases as noises and the non uniform ...

In this paper, we present VSCAN, a novel approach for generating static video summaries. This approach is based on a modified DBSCAN clustering algorithm to summarize the video content utilizing both color and texture features of the video frames. The paper also introduces an enhanced evaluation method that depends on color and texture features. Video Summaries generated by VSCAN are compared w...

The hybrid approach is proposed to show that the clusters also show the swarm behavior. Krill herd algorithm is used to show the simulation of the herding behavior of the krill individuals. Density based approach is used for discovering the clusters and to show the region with sufficiently high density into clusters of krill individuals that of the arbitrary shape in environment. The minimum di...

Synchrophasors are the state-of-the-art measuring devices that sense various parameters such as voltage, current, frequency, and other grid parameters with a high sampling rate. This paper presents an approach to visualize and analyze the smart-grid data generated by synchrophasors using a visualization tool and density based clustering technique. A MATLAB based circle representation tool is ut...

Spatial clustering is an active research area in spatial data mining with various methods reported. In this paper, we compare two density-based methods, DBSCAN and DBRS. First, we briefly describe the methods and then compare them from a theoretical view. Finally, we give an empirical comparison of the algorithms.

In this paper we are going to introduce a new nearest neighbours based approach to clustering, and compare it with previous solutions; the resulting algorithm, which takes inspiration from both DBscan and minimum spanning tree approaches, is deterministic but proves simpler, faster and doesn’t require to set in advance a value for k, the number of clusters.

The ability to monitor students’ academic performance as well as their participation in other events and managing a record for the same is a critical issue to the staff members. Encouraging the students to improve in the area were they are lacking behind is necessary. In this paper we propose a system where the staff members can update the student records in areas like academics, technical skil...

Clustering has been used in many areas. It is an unsupervised learning method which tries to find some distributions and patterns in unlabeled data sets. Although clustering algorithms have been studied for decades, none of them is all purpose. This paper presents a new clustering algorithm, Clustering based on Near Neighbor Influence (CNNI), an improved version in time cost of CNNI algorithm (...