Study on Mutual Information Based Clustering Algorithm
نویسندگان
چکیده
منابع مشابه
Hierarchical Clustering Based on Mutual Information
Motivation: Clustering is a frequently used concept in variety of bioinformatical applications. We present a new method for hierarchical clustering of data called mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X,Y, and Z is equal to the sum of the MI between X and Y , plus th...
متن کاملGene Clustering Based on Clusterwide Mutual Information
Cluster analysis of gene-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and constructing gene regulatory networks. The motivation for considering mutual information is its capacity to measure a general dependence among gene random variables. We propose a novel clustering strategy based on min...
متن کاملCollaborative Filtering Algorithm Based on Mutual Information
Recommender systems are used by E-commerce sites to suggest products to their customers and to provide consumers with information to help them determine which products to purchase. Collaborative filtering algorithm is the most extensive personalized recommendation used in recommender systems. Since not being considering the dependence between predicted item and historical item, typical collabor...
متن کاملInformation-Maximization Clustering Based on Squared-Loss Mutual Information
Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it involves only continuous optimization of model parameters, which is substantially simpler than discrete optimization of cluster assignments. However, existing metho...
متن کاملResearch of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information Technology Journal
سال: 2007
ISSN: 1812-5638
DOI: 10.3923/itj.2007.251.254