List of Articles معیار

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  1 - Document Clustering Based On Ontology and Fuzzy Approach
  Maryam Amiri hasan khatan Lo
  Data mining, also known as knowledge discovery in database, is the process to discover unknown knowledge from a large amount of data. Text mining is to apply data mining techniques to extract knowledge from unstructured text. Text clustering is one of important techniqu More

  Data mining, also known as knowledge discovery in database, is the process to discover unknown knowledge from a large amount of data. Text mining is to apply data mining techniques to extract knowledge from unstructured text. Text clustering is one of important techniques of text mining, which is the unsupervised classification of similar documents into different groups. The most important steps in document clustering are how documents are represented and the measurement of similarities between them. By giving a new ontological representation and a similarity measure, this research focuses on improving the performance of text clustering. The text clustering algorithm has been investigated in three aspects: ontological representation of documents, documents similarity measure, fuzzy inference system to measuring the final similarities. Ultimately, the clustering is carried out by bottom-up hierarchical clustering. In the first step, documents are represented as ontological graph according to domain knowledge. In contrast to keywords method, this method is based on domain concepts and represents a document as subgraph of domain ontology. The extracted concepts of document are the graph nodes. Weight is measured for each node in terms of concept frequency. The relation between documents’ concepts specifies the graph edges and the scope of the concepts’ relation determines the edge’s weight. In the second step, a new similarity measure has been presented proportional to the ontological representation. For each document, main and detailed concepts and main edges are determined. The similarity of each couple of documents is computed in three amounts and according to these three factors. In the third step, the fuzzy inference system with three inputs and one output has been designed. Inputs are the similarities of main concepts, detailed concepts and the main edges of two documents and the output is final similarities of the two documents. In final step, a bottom-up hierarchical clustering algorithm is used to clustering the documents according to final similarity matrix. In order to evaluate, the offered method has been compared with the results of Naïve Bayes method and ontology based algorithms. The results indicate that the proposed method improves the precision, recall, F-measure and accuracy and produces more meaningful results. Manuscript profile
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  2 - Designing a Fuzzy Expert System for Selecting an Appropriate Contractor in Information Technology Outsourcing
  shaban elahi nadia kalantari alireza hassanzade sara shamsollahi
  Increment of complexity and costs of information technology systems have made many problems about infrastructure and manpower for organizations which have been decreased by the use of outsourcing. All organizations try to increase the success of outsourcing projects by More

  Increment of complexity and costs of information technology systems have made many problems about infrastructure and manpower for organizations which have been decreased by the use of outsourcing. All organizations try to increase the success of outsourcing projects by using different ways. One of the important reasons for failure of these projects especially in IT area- because of its major role in acquisition of competitive advantage- is selecting inappropriate contractor. In order to existence of different and contradictive criteria, this selection is complex. The purpose of this research is to determine important criteria and specify the weights of each criterion and finally design a fuzzy expert system for selecting the best contractor in IT outsourcing. The method of knowledge acquisition from experts-which are managers and experts of IT- is a questionnaire. Also in order to evaluate the validity of system, it was used in an IT company. The results show the favorable performance of contractor selection expert system. Manuscript profile
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  3 - Community Detection in Bipartite Networks Using HellRank Centrality Measure
  Ali Khosrozadeh Ali Movaghar Mohammad Mehdi Gilanian Sadeghi Hamidreza Mahyar
  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of ti More

  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it. Manuscript profile
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  4 - Community Detection in Bipartite Networks Using HellRank Centrality Measure
  Ali Khosrozadeh movaghar movaghar Mohammad Mehdi Gilanian Sadeghi Hamidreza Mahyar
  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of ti More

  Community structure is a common and important feature in many complex networks, including bipartite networks. In recent years, community detection has received attention in many fields and many methods have been proposed for this purpose, but the heavy consumption of time in some methods limits their use in large-scale networks. There are methods with lower time complexity, but they are mostly non-deterministic, which greatly reduces their applicability in the real world. The usual approach that is adopted to community detection in bipartite networks is to first construct a unipartite projection of the network and then communities detect in that projection using methods related to unipartite networks, but these projections inherently lose information. In this paper, based on the bipartite modularity measure that quantifies the strength of partitions in bipartite networks and using the HellRank centrality measure, a quick and deterministic method for community detection from bipartite networks directly and without need to projection, proposed. The proposed method is inspired by the voting process in election activities in the social society and simulates it. Manuscript profile