Clustering analysis has received considerable attention in spatial data mining for several years. With the rapid development of the geospatial information technologies, the size of spatial information data is growing exponentially which makes clustering massive spatial data a challenging task. In order to improve the efficiency of spatial clustering for large scale data, many researchers propos...

We propose a new clustering algorithm based upon the maximin correlation analysis (MCA), a learning technique that can minimize the maximum misclassification risk. The proposed algorithm resembles conventional partition clustering algorithms such as k-means in that data objects are partitioned into k disjoint partitions. On the other hand, the proposed approach is unique in that an MCA-based ap...

Clustering is the procedure to group similar objects together. Several algorithms have been proposed for clustering. Among them, the K-means clustering method has less time complexity. But it is sensitive to extreme values and would cause less accurate clustering of the dataset. However, K-medoids method does not have such limitations. But this method uses user-defined value for K. Therefore, i...

The k -medoids methods for modeling clustered data have many desirable properties such as robustness to noise and the ability to use non-numerical values, however, they are typically not applied to large datasets due to their associated computational complexity. In this paper, we present AGORAS, a novel heuristic algorithm for the k -medoids problem where the algorithmic complexity is driven by...

In this work, a new prototype-based clustering method named Evidential C-Medoids (ECMdd), which belongs to the family of medoid-based clustering for proximity data, is proposed as an extension of Fuzzy C-Medoids (FCMdd) on the theoretical framework of belief functions. In the application of FCMdd and original ECMdd, a single medoid (prototype), which is supposed to belong to the object set, is ...

Clustering is a key technique within the KDD process, with k-means, and the more general k-medoids, being well-known incremental partition-based clustering algorithms. A fundamental issue within this class of algorithms is to find an initial set of medians (or medoids) that improves the efficiency of the algorithms (e.g., accelerating its convergence to a solution), at the same time that it imp...


 Penyebaran yang cukup luas dan cepat, membuat pandemi Covid-19 di Sumatera Selatan berdampak negatif pada semua sektor seperti kesehatan, pekerjaan perekonomian. Dengan kebijakan pemerintah mengelompokkan wilayah penanganan menjadi 4 zona, perlu dievaluasi apakah pengelompokkan tersebut sudah tepat menggunakan teknik clustering data mining dengan algoritma K-Means K-Medoids. Dari hasil p...

In this paper an improved hierarchical clustering algorithm by a P system with active membranes is proposed which provides new ideas and methods for cluster analysis. The membrane system has great parallelism. It could reduce the computational time complexity and is suitable for the clustering problem. Firstly an improved hierarchical algorithm was presented which introduced the K-medoids algor...

Most of the existing k-medoid algorithms select initial medoid randomly or use a specific formula based on proximity matrix. This study proposes block-based k-medoids partitioning method for clustering objects. To get medoids, we search an object representative from block standard deviation and sum variable values. We optimized groups to update so this step can reduce number iterations obtain p...