<span lang="EN-US">Few studies on text clustering for the Malay language have been conducted due to some limitations that need be addressed. The purpose of this article is compare two algorithms k-means and k-medoids using Euclidean distance similarity determine which method best documents. Both are applied 1000 documents pertaining housebreaking crimes involving a variety different modus...

Clustering is a technique used in data mining that groups similar objects into one cluster, while dissimilar objects are grouped into different clusters. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. The different partitioning methods studied here are k-means and k-medoids. The different hierarchical t...

Colour analysis is a crucial step in image-based fire detection algorithms. Many of the proposed fire detection algorithms in a still image are prone to false alarms caused by objects with a colour similar to fire. To design a colour-based system with a better false alarm rate, a new colour-differentiating conversion matrix, efficient on images of high colour complexity, is proposed. The elemen...

Recent advances in technology have made our work easier compare to earlier times. Computer network is growing day by day but while discussing about the security of computers and networks it has always been a major concerns for organizations varying from smaller to larger enterprises. It is true that organizations are aware of the possible threats and attacks so they always prepare for the safer...

Clustering is a unsupervised learning technique. This paper presents a clustering based technique that may be applied to Image compression. The proposed technique clusters all the pixels into predetermined number of groups and produces a representative color for each group. Finally for each pixel only clusters number is stored during compression. This technique can be obtained in machine learni...

Background: Analogy-Based Effort Estimation (ABE) is one of the efficient methods for software effort estimation because of its outstanding performance and capability of handling noisy datasets. Problem & Objective: Conventional ABE models usually use the same number of analogies for all projects in the datasets in order to make good estimates. Our claim is that using same number of analogies m...

Temporal pattern provides a novel way to character user behavior in social network from the perspective of time. In this work, we study two types of temporal pattern of user behavior in Micro-blog: Long Term Pattern and Daily Pattern. Long Term Pattern stands for the overall trend of user behavior changes since one starts to use Micro-blog and it provides the global view of user behavior variat...

Scalable data mining algorithms have become crucial to efficiently support KDD processes on large databases. In this paper, we address the task of scaling up k-medoids based algorithms through the utilization of metric access methods, allowing clustering algorithms to be executed by database management systems in a fraction of the time usually required by the traditional approaches. Experimenta...

For this project, we attempted to evaluate the effectiveness of rudimentary sequence mining techniques for characterizing I/O trace data. We have taken trace information for a scientific application running on a cluster, and we have attempted to use K-Medoids based clustering algorithms to correlate particular trace sequences with phases of application execution. We also present a novel approac...

In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...