with rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. because o...

This paper analyzes the effects of network positions and individual risk attitudes on individuals’ strategic decisions in an experiment where actions are strategic substitutes. The game theoretic basis for our experiment is the model of Bramoullé and Kranton (2007). In particular, we are interested in disentangling the influence of global, local and individual factors. We study subjects’ strate...

Small-world networks, i.e. networks displaying both a high clustering coefficient and a small characteristic path length, are obliquitous in nature. Since their identification, the “small-worldness” metric, as proposed by Humphries and Gurney, has frequently been used to detect such structural property in real-world complex networks, to a large extent in the study of brain dynamics. Here I disc...

Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer’s disease (AD), but not its inter-regional covariation. We present novel features based on the inter-regional co-variation of cortical thickness. Initially the cortical labels of each patient is partitioned into small patches (graph nodes) by spatial k-means clust...

In this paper, we propose a simple rule that generates scale-free small-world networks with tunable assortative coefficient. These networks are constructed by two-stage adding process for each new node. The model can reproduce scale-free degree distributions and small-world effect. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the...

We present an algorithm that generates networks in which the skewness of the degree distribution is tuneable by modifying the preferential attachment step of the Barabási-Albert construction algorithm. Skewness is linearly correlated with the maximal degree of the network and, therefore, adequately represents the influence of superspreaders or hubs. By combining our algorithm with work of Holme...

Clustering merupakan proses pengelompokan sekumpulan data ke dalam klaster yang memiliki kemiripan. Kemiripan satau ditentukan dengan perhitungan jarak. Untuk melihat perfoma beberapa jarak, penelitian ini penulis menguji pada 6 atribut berbeda, yakni 2, 3, 4, dan atribut. Dari hasil uji perbandingan rumus jarak K-Means clustering menggunakan Silhouette coefficient dapat disimpulkan bahwa: 1) C...