Classification of Cerebral Infarction Data Using K-Means and Kernel K-Means

Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...

Diabetes Classification Using K-means

This paper presents a survey on proposed quantized techniques in medical field to understand which age group of people are being affecting by diabetes. The overall aspiration of the diabetes data mining procedure is to extract information from diabetes data set and convert it into a comprehensible arrangement for further use. This paper deals with diabetes and proposed a data mining system usin...

Scalable Kernel Clustering: Approximate Kernel k-means

Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data. Among various kernel-based clustering algorithms, kernel k -means has gained popularity due to its simple iterative nature and ease of implementation. However, its run-time complexity and memory footprint increase quadratically in terms of the size of the data set, and hence, large data s...

Segmentation of Photospheric Solar Images by Using c-Means,k-Means, and FCM Algorithms

Kernel Penalized K-means: A feature selection method based on Kernel K-means

Article history: Received 11 June 2014 Received in revised form 23 October 2014 Accepted 11 June 2015 Available online 19 June 2015