Visual Sampling Based Clustering Algorithm VSC

DENDIS: A new density-based sampling for clustering algorithm

To deal with large datasets, sampling can be used as a preprocessing step for clustering. In this paper, an hybrid sampling algorithm is proposed. It is density-based while managing distance concepts to ensure space coverage and fit cluster shapes. At each step a new item is added to the sample: it is chosen as the furthest from the representative in the most important group. A constraint on th...

ON FUZZY NEIGHBORHOOD BASED CLUSTERING ALGORITHM WITH LOW COMPLEXITY

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...

Sampling-based Sub-Trajectory Clustering

An Adaptive LEACH-based Clustering Algorithm for Wireless Sensor Networks

LEACH is the most popular clastering algorithm in Wireless Sensor Networks (WSNs). However, it has two main drawbacks, including random selection of cluster heads, and direct communication of cluster heads with the sink. This paper aims to introduce a new centralized cluster-based routing protocol named LEACH-AEC (LEACH with Adaptive Energy Consumption), which guarantees to generate balanced cl...

A parallel sampling based clustering

—The problem of automatically clustering data is an age old problem. People have created numerous algorithms to tackle this problem. The execution time of any of this algorithm grows with the number of input points and the number of cluster centers required. To reduce the number of input points we could average the points locally and use the means or the local centers as the input for clusterin...