Sampling is a data reduction technique that handles data volume-related challenges and increases the speed, scalability, flexibility, accuracy, quality, efficiency, and utilizes memory resources for any data mining algorithms without the influence of their characteristics. This paper proposed the ...
entitled Partitional Clustering Algorithms to be published by Springer sometime in late 2014. Below is a short description of the volume: Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering i...
Clustering is the process of organizing objects into groups whose members are similar in some way. A variety of algorithms have recently emerged that meet these requirements and were successfully applied to real life data mining problem. Fuzzy c-means (FCM) and k-means are commonly used ...
A Medoid-based Method for Clustering Categorical Data Medoid-based method is an alternative technique to centroid-based method for partitional clustering algorithms. This method has been incorporated in a re... A Seman,ZA Bakar,AM Sapawi,... - 《Journal of Artificial Intelligence》 被引量: 2发...
J. Prakash, P.K. Singh, Partitional algorithms for hard clustering using evolutionary and swarm intelligence methods: a survey, Advances in Intelligent Systems and Computing, 2, 515-528 (2013)J. Prakash, P.K. Singh. Partitional Algorithms for Hard Clustering Using Evolutionary and Swarm ...
The performance of the MDSHKM algorithm is compared with the KM and KM++ algorithms through R square, Root-Mean-Square Standard Deviation, Davies鈥揃ouldin score, Calinski Harabasz score, Silhouette Coefficient, Number of Iterations and CPU time validation indices using eight real datasets. The ...
% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.Originality/valueThe KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data....
The importance of clustering algorithms in transportation data has been illustrated in previous research. This paper compares the effect of different distance/similarity measures on a partitional clustering algorithm kmedoid(PAM) using transportation dataset. A recently developed data mining open source ...
Now, it has been widely used in data mining, pattern recognition, image processing and so on. However, due to different settings of the parameters and random selection of initial centers, traditional clustering algorithms may produce different clustering partitions for a single dataset. Clustering ...
The criterion function may emphasize the local or global structure of the data andits optimization is an iterative procedure. The intention to analyze the fact that partitional clusteringalgorithms performs efficiently for numerical attribute rather than categorical attribute. To analyze thealgorithm best ...