Optimizing k-means for Scalability

Optimizing k-means for Scalability

Proposed decades ago, k-means is still the most popular algorithm for clustering. Despite the drawbacks of k-means, its advantages make it most attractive. Several researches have been conducted to alleviate the problems of k-means. We suggest here some simple modifications to optimize k-means for scalability without much sacrifice in the precision. Current shift in emphasis of data mining towa...

Optimizing K-means in Cascading Clustering and Classification

Optimizing K-Means by Fixing Initial Cluster Centers

Data mining techniques help in business decision making and predicting behaviors and future trends. Clustering is a data mining technique used to make groups of objects that are somehow similar in characteristics. Clustering analyzes data objects without consulting a known class label or category i.e. it is an unsupervised data mining technique. Kmeans is a widely used partitional clustering al...

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...

K-Medoids For K-Means Seeding

We run experiments showing that algorithm clarans (Ng et al., 2005) finds better Kmedoids solutions than the standard algorithm. This finding, along with the similarity between the standard K-medoids and K-means algorithms, suggests that clarans may be an effective K-means initializer. We show that this is the case, with clarans outperforming other popular seeding algorithms on 23/23 datasets w...