The k-means algorithm is a widely used clustering method in pattern recognition and machine learning due to its simplicity to implement and low time complexity. However, it has the following main drawbacks: 1) the number of clusters, k, needs to be provided by the user in advance, 2) it can easily reach local minima with randomly selected initial centers, 3) it is sensitive to outliers, and 4) ...

This paper presents a feature level fusion approach which uses the improved K-medoids clustering algorithm and isomorphic graph for face and palmprint biometrics. Partitioning around medoids (PAM) algorithm is used to partition the set of n invariant feature points of the face and palmprint images into k clusters. By partitioning the face and palmprint images with scale invariant features SIFT ...

Karakteristik lahan kritis meliputi kerusakan struktur tanah, penurunan kuantitas dan kualitas bahan organik, kekurangan unsur hara, terganggunya siklus hidrologi. Lahan membutuhkan rehabilitasi peningkatan produktivitas untuk kembali ke keadaan sebelumnya sebagai ekosistem yang sehat atau menghasilkan hasil lebih baik. Perambahan hutan, penebangan liar, kebakaran penggunaan sumber daya hutan t...

Earlier research has resulted in the production of an ‘allrules’ algorithm for data-mining that produces all conjunctive rules of above given confidence and coverage thresholds. While this is a useful tool, it may produce a large number of rules. This paper describes the application of two clustering algorithms to these rules, in order to identify sets of similar rules and to better understand ...

The paper touches upon the problem of implementation Partition Around Medoids (PAM) clustering algorithm for the Intel Many Integrated Core architecture. PAM is a form of well-known k-Medoids clustering algorithm and is applied in various subject domains, e.g. bioinformatics, text analysis, intelligent transportation systems, etc. An optimized version of PAM for the Intel Xeon Phi coprocessor i...

k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with n points into a set of k disjoint clusters. However, k-medoids itself requires all distances between data points that are not so easy to get in many applications. In this paper, we introduce a new method which requires only a sma...