• Title, Summary, Keyword: Clustering

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A Mixed Co-clustering Algorithm Based on Information Bottleneck

  • Liu, Yongli;Duan, Tianyi;Wan, Xing;Chao, Hao
    • Journal of Information Processing Systems
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    • v.13 no.6
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    • pp.1467-1486
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    • 2017
  • Fuzzy co-clustering is sensitive to noise data. To overcome this noise sensitivity defect, possibilistic clustering relaxes the constraints in FCM-type fuzzy (co-)clustering. In this paper, we introduce a new possibilistic fuzzy co-clustering algorithm based on information bottleneck (ibPFCC). This algorithm combines fuzzy co-clustering and possibilistic clustering, and formulates an objective function which includes a distance function that employs information bottleneck theory to measure the distance between feature data point and feature cluster centroid. Many experiments were conducted on three datasets and one artificial dataset. Experimental results show that ibPFCC is better than such prominent fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC and FCCI, in terms of accuracy and robustness.

Tree-structured Clustering for Continuous Data (연속형 자료에 대한 나무형 군집화)

  • Huh Myung-Hoe;Yang Kyung-Sook
    • The Korean Journal of Applied Statistics
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    • v.18 no.3
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    • pp.661-671
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    • 2005
  • The aim of this study is to propose a clustering method, called tree-structured clustering, by recursively partitioning continuous multivariate dat a based on overall $R^2$ criterion with a practical node-splitting decision rule. The clustering method produces easily interpretable clustering rules of tree types with the variable selection function. In numerical examples (Fisher's iris data and a Telecom case), we note several differences between tree-structured clustering and K-means clustering.

Clustering Routing Algorithms In Wireless Sensor Networks: An Overview

  • Liu, Xuxun;Shi, Jinglun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.7
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    • pp.1735-1755
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    • 2012
  • Wireless sensor networks (WSNs) are becoming increasingly attractive for a variety of applications and have become a hot research area. Routing is a key technology in WSNs and can be coarsely divided into two categories: flat routing and hierarchical routing. In a flat topology, all nodes perform the same task and have the same functionality in the network. In contrast, nodes in a hierarchical topology perform different tasks in WSNs and are typically organized into lots of clusters according to specific requirements or metrics. Owing to a variety of advantages, clustering routing protocols are becoming an active branch of routing technology in WSNs. In this paper, we present an overview on clustering routing algorithms for WSNs with focus on differentiating them according to diverse cluster shapes. We outline the main advantages of clustering and discuss the classification of clustering routing protocols in WSNs. In particular, we systematically analyze the typical clustering routing protocols in WSNs and compare the different approaches based on various metrics. Finally, we conclude the paper with some open questions.

On hierarchical clustering in sufficient dimension reduction

  • Yoo, Chaeyeon;Yoo, Younju;Um, Hye Yeon;Yoo, Jae Keun
    • Communications for Statistical Applications and Methods
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    • v.27 no.4
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    • pp.431-443
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    • 2020
  • The K-means clustering algorithm has had successful application in sufficient dimension reduction. Unfortunately, the algorithm does have reproducibility and nestness, which will be discussed in this paper. These are clear deficits for the K-means clustering algorithm; however, the hierarchical clustering algorithm has both reproducibility and nestness, but intensive comparison between K-means and hierarchical clustering algorithm has not yet been done in a sufficient dimension reduction context. In this paper, we rigorously study the two clustering algorithms for two popular sufficient dimension reduction methodology of inverse mean and clustering mean methods throughout intensive numerical studies. Simulation studies and two real data examples confirm that the use of hierarchical clustering algorithm has a potential advantage over the K-means algorithm.

Exponential Probability Clustering

  • Yuxi, Hou;Park, Cheol-Hoon
    • Proceedings of the IEEK Conference
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    • pp.671-672
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    • 2008
  • K-means is a popular one in clustering algorithms, and it minimizes the mutual euclidean distance among the sample points. But K-means has some demerits, such as depending on initial condition, unsupervised learning and local optimum. However mahalanobis distancecan deal this case well. In this paper, the author proposed a new clustering algorithm, named exponential probability clustering, which applied Mahalanobis distance into K-means clustering. This new clustering does possess not only the probability interpretation, but also clustering merits. Finally, the simulation results also demonstrate its good performance compared to K-means algorithm.

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Transactions Clustering based on Item Similarity (아이템의 유사도를 고려한 트랜잭션 클러스터링)

  • 이상욱;김재련
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • pp.250-257
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    • 2002
  • Clustering is a data mining method, which consists in discovering interesting data distributions in very large databases. In traditional data clustering, similarity of a cluster of object is measured by pairwise similarity of objects in that paper. In view of the nature of clustering transactions, we devise in this paper a novel measurement called item similarity and utilize this to perform clustering. With this item similarity measurement, we develop an efficient clustering algorithm for target marketing in each group.

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Path based K-means Clustering for RFID Data Sets

  • Yun, Hong-Won
    • Journal of information and communication convergence engineering
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    • v.6 no.4
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    • pp.434-438
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    • 2008
  • Massive data are continuously produced with a data rate of over several terabytes every day. These applications need effective clustering algorithms to achieve an overall high performance computation. In this paper, we propose ancestor as cluster center based approach to clustering, the K-means algorithm using ancestor. We modify the K-means algorithm. We present a clustering architecture and a clustering algorithm that minimize of I/Os and show a performance with excellent. In our experimental performance evaluation, we present that our algorithm can improve the I/O speed and the query processing time.

Document Clustering Using Reference Titles (인용문헌 표제를 이용한 문헌 클러스터링에 관한 연구)

  • Choi, Sang-Hee
    • Journal of the Korean Society for information Management
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    • v.27 no.2
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    • pp.241-252
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    • 2010
  • Titles have been regarded as having effective clustering features, but they sometimes fail to represent the topic of a document and result in poorly generated document clusters. This study aims to improve the performance of document clustering with titles by suggesting titles in the citation bibliography as a clustering feature. Titles of original literature, titles in the citation bibliography, and an aggregation of both titles were adapted to measure the performance of clustering. Each feature was combined with three hierarchical clustering methods, within group average linkage, complete linkage, and Ward's method in the clustering experiment. The best practice case of this experiment was clustering document with features from both titles by within-groups average method.

A Multi-Dimensional Issue Clustering from the Perspective Consumers' Interests and R&D (소비자 선호 이슈 및 R&D 관점에서의 다차원 이슈 클러스터링)

  • Hyun, Yoonjin;Kim, Namgyu;Cho, Yoonho
    • Journal of Information Technology Services
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    • v.14 no.1
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    • pp.237-249
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    • 2015
  • The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers' interests, and (ii) related R&D terms.

Fuzzy Clustering Algorithm for Web-mining (웹마이닝을 위한 퍼지 클러스터링 알고리즘)

  • Lim, Young-Hee;Song, Ji-Young;Park, Dai-Hee
    • Journal of Korean Institute of Intelligent Systems
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    • v.12 no.3
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    • pp.219-227
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    • 2002
  • The post-clustering algorithms, which cluster the result of Web search engine, have some different requirements from conventional clustering algorithms. In this paper, we propose the new post-clustering algorithm satisfying those of requirements as many as possible. The proposed fuzzy Concept ART is the form of combining the concept vector having several advantages in document clustering with fuzzy ART known as real time clustering algorithms on the basis of fuzzy set theory. Moreover we show that it can be applicable to general-purpose clustering as well as post clustering.