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K-modes is a typical categorical clustering algorithm. Firstly, we improve the process of K-modes: when allocating categorical objects to clusters, the number of each attribute item in clusters is updated, so that the new modes of clusters can be computed after reading the whole dataset once. In order to make K-modes capable for large-scale categorical data, we then implement K-modes on Hadoop using...
Latent Dirichlet Allocation (LDA) has been widely applied to text mining. LDA is a probabilistic topic model which processes documents as the probability distribution of topics. One challenging issue in application of LDA is to select the optimal number of topics in LDA model. This paper presents a topic selection method which considers the density of each topic and computes the most unstable topic...
MapReduce is increasingly considered as a useful parallel programming model for large-scale data processing. It exploits parallelism among execution of primitive map and reduce operations. Hadoop is an open source implementation of MapReduce that has been used in both academic research and industry production. However, its implementation strategy that one map task processes one data block limits the...
Social Networks Service (SNS), is becoming more and more popular and a lot of studies have been carried out in this active field. However, traditional analysis methods based on single machines is not suitable because the network is growing too large. MapReduce, a programming paradigm proposed by Google, gives us a new approach to solve large-scale social networks analysis problem by making use of...
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