Partitional data clustering with K-means algorithm is the dividing of objects into smaller and
disjoint groups that has the most similarity with objects in a group and most dissimilarity from …

Clustering is an important data analysis technique, which has been applied to many
practical scenarios. However, many partitioning based clustering algorithms are sensitive to …

Effective management of customer's knowledge leads to efficient Customer Relationship
Management (CRM). To accurately predict customer's behaviour, clustering, especially K …

The aim of this paper is to introduce a novel prototype generation technique for handwriting
digit recognition. Prototype generation is approached as a two-stage process. The first stage …

The nearest neighbor rule identifies the category of an unknown element according to its
known nearest neighbors' categories. This technique is efficient in many fields as event …

The growing field of mobile telecommunication becomes more and more competitive in the
world. Therefore, the mobile operators are facing tremendous challenges. The customer …

This paper proposes a novel evolutionary clustering algorithm and prepares eligible initial
centroids for K-Means algorithm by global search approach of efficient hybrid knowledge of …

Among the data clustering algorithms, k-means (KM) algorithm is one of the most popular
clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to …

Among the data clustering algorithms, the k-means (KM) algorithm is one of the most
popular clustering techniques because of its simplicity and efficiency. However, KM is …

Clustering is a process of grouping same objects into a specified number of clusters. K-
means and K-medoids algorithms are the most popular partitional clustering techniques for …