Comparing Entropy Weighting Method and AHP for JIT implementation in a Manufacturing System

Although some important criteria, such as work in process (WIP) and inventory, are recognized to have an impact on Just-in-Time (JIT) implementations, the exact weights of these criteria for different systems are not known. Consequently, the decision maker will not be able to predict the size of change in the system when implementing his JIT strategy. On the other hand, different weighting methods result in different weight values which makes it more confusing for the decision maker. We therefore consider entropy weighting method and Analytic Hierarchy Process (AHP) to compute the weights of the selected criteria. A case study is also discussed to demonstrate the differences between these two weighting methods. Simulation modeling is used to validate and compare the results

Comparison of Telecommunication Markets in Europe using Multivariate Statistical Analysis

Common problem in valuation of telecommunication companies is finding comparable data and markets for valuation. The aim of this work was to identify comparable markets for the telecommunication market in Europe. A method for comparison of the markets based on the Multivariate Statistical Analysis was presented. The study covers twenty-two European countries. Using taxonomic measures, these countries were divided into five groups, taking into account the following variables: average monthly service cost of the fixed Internet, average cost of the mobile usage, and average cost of the fixed telephony usage. Within individual groups, the costs of telecommunications services are less diverse than in the entire population; their members can be considered comparable markets. The same method can be used for comparing markets in cases of enterprise valuations in the telecommunication sector, and also in analysis of their level of development

Graph based text representation for document clustering

Advances in digital technology and the World Wide Web has led to the increase of digital documents that are used for various purposes such as publishing and digital library. This phenomenon raises awareness for the requirement of effective techniques that can help during the search and retrieval of text. One of the most needed tasks is clustering, which categorizes documents automatically into meaningful groups. Clustering is an important task in data mining and machine learning. The accuracy of clustering depends tightly on the selection of the text representation method. Traditional methods of text representation model documents as bags of words using term-frequency index document frequency (TFIDF). This method ignores the relationship and meanings of words in the document. As a result the sparsity and semantic problem that is prevalent in textual document are not resolved. In this study, the problem of sparsity and semantic is reduced by proposing a graph based text representation method, namely dependency graph with the aim of improving the accuracy of document clustering. The dependency graph representation scheme is created through an accumulation of syntactic and semantic analysis. A sample of 20 news group, dataset was used in this study. The text documents undergo pre-processing and syntactic parsing in order to identify the sentence structure. Then the semantic of words are modeled using dependency graph. The produced dependency graph is then used in the process of cluster analysis. K-means clustering technique was used in this study. The dependency graph based clustering result were compared with the popular text representation method, i.e. TFIDF and Ontology based text representation. The result shows that the dependency graph outperforms both TFIDF and Ontology based text representation. The findings proved that the proposed text representation method leads to more accurate document clustering results

Sparsity-Inducing Fuzzy Subspace Clustering

This paper considers a fuzzy subspace clustering problem and proposes to introduce an original sparsity-inducing regularization term. The minimization of this term, which involves a l$_{0}$ penalty, is considered from a geometric point of view and a novel proximal operator is derived. A subspace clustering algorithm, Prosecco, is proposed to optimize the cost function using both proximal and alternate gradient descent. Experiments comparing this algorithm to the state of the art in sparse fuzzy subspace clustering show the relevance of the proposed approach

Study of subspace clustering algorithm of high dimensional data based on variable weighting methods

高维数据的稀疏性和“维灾“问题使得多数传统聚类算法失去作用,因此研究高维数据集的聚类算法己成为当前的一个热点。子空间聚类算法是实现高维数据集聚类的有效方法之一。介绍并实现了基于可变加权的高维数据子空间聚类算法SCAd和EWkM,并分别对人造数据、现实数据等数据集进行测试,根据测试结果进行分析,对比两种算法的性能及适用场合。The sparsity and the problem of the curse of dimensionality of high-dimensional data, make the most of traditional clustering algorithms lose their action in high-dimensional space.Therefore, clustering of data in a high-dimensional space becomes a hot research area.Subspace clustering algorithm is one of the effective ways to handle problems of high-dimensional data clustering.This paper introduces and realizes two algorithms (SCAD and EWKM) that discover clusters in subspaces spanned by different combinations of dimensions via local weightings of features.We experiment these algorithms using synthetic datasets and real datasets, then analyze the results and contrast their performance and applicable occasions