Piwi-interacting RNAs (piRNAs) are recently discovered, endogenous small non-coding RNAs. piRNAs protect the genome from invasive transposable elements (TE) and sustain integrity of the genome in germ cell lineages. Small RNA-sequencing data can be used to detect piRNA activations in a cell under a specific condition. However, identification of cell specific piRNA activations requires sophistic...

To solve the problem of distributed data streams clustering, the algorithm DB-DDSC (Density-Based Distribute Data Stream Clustering) was proposed. The algorithm consisted of two stages. First presented the concept of circular-point based on the representative points and designed the iterative algorithm to find the densityconnected circular-points, then generated the local model at the remote si...

This paper shows how it made possible in geographical science to observe the seismic zone, clustering of highly sensitive earthquake zone and spatial data clustering during important geographical processes. This paper shows simple density based and KMean clustering technique. Density-Based clustering is done here using density estimation and by searching regions which are denser than a given th...

This document describes the implementation of two density-based clustering algorithms: DBSCAN [Ester1996] and SNN [Ertoz2003]. These algorithms were implemented within the context of the LOCAL project [Local2005] as part of a task that aims to create models of the geographic space (Space Models) to be used in context-aware mobile systems. Here, the role of the clustering algorithms is to identi...

Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone, that causes clumping or clustering behavior in a species and brings individuals into a closer proximity, is called aggregation pheromone. This article presents a new algorithm (called, APC) for clustering data sets based on this property of aggreg...

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 article describes a novel way of representing large databases of shapes. We propose a hierarchical clustering of a set of Fourier-transformed contours. The clustering analysis is density-based and is performed using topographic maps. We have tested the approach on a database of extracted contours of marine animals, generated by Mokhtarian and co-workers. The resulting clusters group contou...

Data clustering plays an important role in various fields. Data clustering approaches have been presented in recent decades. Identifying clusters with widely differing shapes, sizes and densities in the presence of noise and outliers is challenging. Many density-based clustering algorithms, such as DBSCAN, can locate arbitrary shapes, sizes and filter noise, but cannot identify clusters based o...

Original scientific paper As density-based algorithm, Density Peak Clustering (DPC) algorithm has superiority of clustering by finding the density peaks. But the cut-off distance and clustering centres had to be set at random, which would influence clustering outcomes. Fruit flies find the best food by local searching and global searching. The food found was the parameter extreme value calculat...