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1st International Conference on Technical Advancements in Computers and Communications (ICTACC)

作者： Rajkamal, S. Arunai Engn Coll AP Dept IT Tiruvannamalai India

ISBN: (纸本)9781509047970

In many governments and private institutions, one of the major tasks is to select the best project proposals for allocating the fund. These funding organizations select the proposals by submitting them to the reviewers for review. Manual process is too difficult when the number of projects is more. The earlier models introduced ontology based Text mining methods to cluster the proposals of any language without considering the reviewer's expertise with respect to their domain. The proposed method identifies the main topic of project in a hasty manner by using ontology based topic identification algorithm. It uses EM algorithm to group the proposals based on their domain, issues and technology for selecting the expertise in the domain for review. This approach gives better performance by allocating proposal to the appropriate reviewers.

关键词： Ontology Text mining clustering Classification expectation maximization clustering algorithm

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Data clustering Approaches Survey and Analysis 1

Data Clustering Approaches Survey and Analysis

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International Conference On Futuristic Trends in Computational Analysis and Knowledge Management

作者： Ahalya, G. Pandey, Hari Mohan Amity Univ ASET CSE Dept Noida India

ISBN: (纸本)9781479984336

In the current world, there is a need to analyze and extract information from data. clustering is one such analytical method which involves the distribution of data into groups of identical objects. Every group is known as a cluster, which consists of objects that have affinity within the cluster and disparity with the objects in other groups. This paper is intended to examine and evaluate various data clustering algorithms. The two major categories of clustering approaches are partition and hierarchical clustering. The algorithms which are dealt here are: k-means clustering algorithm, hierarchical clustering algorithm, density based clustering algorithm, self-organizing map algorithm, and expectation maximization clustering algorithm. All the mentioned algorithms are explained and analyzed based on the factors like the size of the dataset, type of the data set, number of clusters created, quality, accuracy and performance. This paper also provides the information about the tools which are used to implement the clustering approaches. The purpose of discussing the various software/tools is to make the beginners and new researchers to understand the working, which will help them to come up with new product and approaches for the improvement.

关键词： clustering expectation maximization clustering algorithm Hierarchical clustering K-means clustering algorithm Self-organization maps algorithm density based clustering algorithm

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Exploring an Objective Weighting System for Travel & Tourism Pillars

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Procedia - Social and Behavioral Sciences 2012年 57卷 183-192页

作者： L.W. Lan W.W. Wu Y.T. Lee Department of Television & Internet Marketing Management Ta Hwa Institute of Technology Taiwan Department of International Trade Ta Hwa Institute of Technology Taiwan

The World Economic Forum employs Travel & Tourism Competitiveness Indexes (TTCI) to measure the travel & tourism (T&T) global competitiveness of a country. The TTCI overall scores are calculated with an arithmetic mean aggregation from the scores of the fourteen composite pillars with a subjective assumption of all the pillars having the same weights. This paper attempts to release such a subjective assumption by proposing a new solution framework to explore an objective weighting system for the pillars. The proposed solution framework employs the expectation maximization (EM) clustering algorithm to group the 139 ranked countries into three classes and then performs the Artificial Neural Network (ANN) analysis to explore the objective weighting system for the fourteen pillars. The results show that tourism infrastructure, ground transport infrastructure, air transport infrastructure, cultural resources, health and hygiene, and ICT infrastructure are the six most critical pillars contributing to the TTCI overall scores. Accordingly, the policy makers should allocate limited resources with priority to improve these six pillars to frog leap the T&T global competitiveness.

关键词： Artificial Neural Network expectation maximization clustering algorithm objective weightings travel & tourism

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