% 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....
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 ...
Application of Data Mining Clustering the Development of Covid-19 Using K-Medoids Method At the beginning of March, Indonesia was hit by the entry of the corona virus (covid) outbreak. Every day the cases of the spread of covid-19 in Indonesia ... N Gusmantoni - 《Journal of Computer...
Therefore, the nature of classical data changes into big data, and mining techniques have to face high computation cost, performance and scalability-related challenges. The K-means (KM) algorithm is the most widely used partitional clustering approach that depends on K clusters, initial centroid, ...
This paper proposed the systematic sampling-based big data mining model through the K-means clustering that is known as SYK-means (systematic sampling-based K-means). The experimental results of the SYK-means algorithm are compared with the RSK-means (random sampling-based K-means) and ...
The operation is needed in a number of data mining tasks such as unsupervised classification anddata summation as well as segmentation of large heterogeneous data sets into smaller homogeneous subsetsthat can be easily managed, separately modeled and analyzed. Clustering is a popular approach used to...
Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering...
The proposal made in this research work evaluates the institutional quality in imparting education to the students using data mining techniques such as clusters, and predictive mining. It develops a clustering scheme to segment the student grades based on the knowledge acquired. In addition, it ...
Clustering uncertain data has emerged as a challenging task in uncertain data management and mining. Thanks to a computational complexity advantage over other clustering paradigms, partitionalclustering has been particularly studied and a number of algorithms have been developed. While existing proposals ...