Performance Evaluation of Simple K-Mean and Parallel K-Mean Clustering Algorithms: Big Data Business Process Management Concept

Comparative Analysis of Hybrid K-Mean Algorithms on Data Clustering

Data clustering is a process of organizing data into certain groups such that the objects in the one cluster are highly similar but dissimilar to the data objects in other clusters. K-means algorithm is one of the popular algorithms used for clustering but k-means algorithm have limitations like it is sensitive to noise ,outliers and also it does not provides global optimum results. To overcome...

Clustering of Data Using K-Mean Algorithm

Clustering is associate automatic learning technique geared toward grouping a collection of objects into subsets or clusters. The goal is to form clusters that are coherent internally, however well completely different from one another. In plain words, objects within the same cluster ought to be as similar as potential, whereas objects in one cluster ought to be as dissimilar as potential from ...

k-equitable mean labeling

Comparison between Standard K-Mean Clustering and Improved K-Mean Clustering

Clustering in data mining is very important to discover distribution patterns and this importance tends to increase as the amount of data grows. It is one of the main analytical methods in data mining and its method influences its results directly. K-means is a typical clustering algorithm[3]. It mainly consists of two phases i.e. initializing random clusters and to find the nearest neighbour. ...

Performance Evaluation with K-Mean and K-Mediod in Data Mining

Data mining is the process of extraction of various types of information from different types of dataset that contains various types of attributes. Clustering is an approach that divides the whole information into different clusters. After processing of division of data values into different clusters centeroid have been computed. Cluster centeroid has been done on the basis of distance from oth...