An adjustable p-exponential clustering algorithm

نویسندگان

Valmir Macário Filho

Francisco de A. T. de Carvalho

چکیده

This paper proposes a new exponential clustering algorithm (XPFCM) by reformulating the clustering objective function with an additional parameter p to adjust the exponential behavior for membership assignment. The clustering experiments show that the proposed method assign data to the clusters better than other fuzzy C-means (FCM) variants.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Generating Scale-free Networks with Adjustable Clustering Coefficient Via Random Walks

This paper presents an algorithm for generating scale-free networks with adjustable clustering coefficient. The algorithm is based on a random walk procedure combined with a triangle generation scheme which takes into account genetic factors; this way, preferential attachment and clustering control are implemented using only local information. Simulations are presented which support the validit...

متن کامل

An Incremental DC Algorithm for the Minimum Sum-of-Squares Clustering

Here, an algorithm is presented for solving the minimum sum-of-squares clustering problems using their difference of convex representations. The proposed algorithm is based on an incremental approach and applies the well known DC algorithm at each iteration. The proposed algorithm is tested and compared with other clustering algorithms using large real world data sets.

متن کامل

An Optimization K-Modes Clustering Algorithm with Elephant Herding Optimization Algorithm for Crime Clustering

The detection and prevention of crime, in the past few decades, required several years of research and analysis. However, today, thanks to smart systems based on data mining techniques, it is possible to detect and prevent crime in a considerably less time. Classification and clustering-based smart techniques can classify and cluster the crime-related samples. The most important factor in the c...

متن کامل

An Improved SSPCO Optimization Algorithm for Solve of the Clustering Problem

Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. Data clustering is the process of grouping data into a number of clusters. The goal of data clustering is to make the data in the same cluster share a high degree of similarity while being very dissimilar to data from other clusters. Clustering algorithms have been applied to a ...

متن کامل