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 (...

The effectiveness and efficiency of the existing cluster analysis methods are limited, especially when the referred data has high dimensions or when the clusters within the data are not well-separated and having different densities, sizes and shapes. Density-based clustering algorithms have been proven able to discovered clusters with those characteristics. Previous researchers which explored d...

CFSFDP (clustering by fast search and find of density peaks) is recently developed density-based clustering algorithm. Compared to DBSCAN, it needs less parameters and is computationally cheap for its noniteration. Alex. at al have demonstrated its power by many applications. However, CFSFDP performs not well when there are more than one density peak for one cluster, what we name as "no density...

This paper presents a comparative study of three Density based Clustering Algorithms that are DENCLUE, DBCLASD and DBSCAN. Six parameters are considered for their comparison. Result is supported by firm experimental evaluation. This analysis helps in finding the appropriate density based clustering algorithm in variant situations. General Terms Algorithms .

This paper intend to present an approach to analyse the change of word meaning based on word embedding, which is a more general method to quantize words than before. Through analysing the similar words and clustering in different period, semantic change could be detected. We analysed the trend of semantic change through density clustering method called DBSCAN. Statics and data visualization is ...