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Scalable Fuzzy Algorithms for Data Management and Analysis最新文献

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Fuzzy Cluster Analysis of Larger Data Sets 大型数据集的模糊聚类分析
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 2009-10-01 DOI: 10.4018/978-1-60566-858-1.CH012
R. Winkler, F. Klawonn, F. Höppner, R. Kruse
{"title":"Fuzzy Cluster Analysis of Larger Data Sets","authors":"R. Winkler, F. Klawonn, F. Höppner, R. Kruse","doi":"10.4018/978-1-60566-858-1.CH012","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH012","url":null,"abstract":"The application of fuzzy cluster analysis to larger data sets can cause runtime and memory overflow problems. While deterministic or hard clustering assigns a data object to a unique cluster, fuzzy clustering distributes the membership of a data object over different clusters. In standard fuzzy clustering, membership degrees will (almost) never become zero, so that all data objects are assigned to − even with very small membership degrees − all clusters. As a consequence, this does not only demand higher computational and memory power, it also leads to the undesired effect that all data objects will always influence all clusters, no matter how far away they are from a cluster. New approaches, modifying the idea of the fuzzifier, have been developed to avoid the problem of nonzero membership degrees for all data and clusters. In this paper, these ideas will be combined with concepts of speeding up fuzzy clustering by a suitable data organization, so that fuzzy clustering can be applied more efficiently to larger data sets.","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"157 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114000744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Human Focused Summarizing Statistics Using OWA Operators 使用OWA操作符以人为中心汇总统计信息
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-858-1.CH009
R. Yager
{"title":"Human Focused Summarizing Statistics Using OWA Operators","authors":"R. Yager","doi":"10.4018/978-1-60566-858-1.CH009","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH009","url":null,"abstract":"While many applications make use of the Ordered Weighted Averaging (OWA) operator (Yager, 1988) one under-explored application has been in summarizing data sets. We note that formally the OWA operator can be used to model different types of summarizing statistics depending on the choice of OWA weighting vector. Summarizing statistics are of particular importance in the field of data management and analysis and data mining (Tan, Steinbach & Kumar, 2006; Bouchon-Meunier, Rifqi & Lesot, 2008). Among the most well known summarizing statistics are the average, median and mode. While these have been extremely useful they don’t completely enable the kinds of sophisticated analysis desired by modern AbsTRACT","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121816447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Scaling Fuzzy Models 缩放模糊模型
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-858-1.CH002
L. Hall, Dmitry Goldgof, Juana Canul-Reich, Prodip Hore, Weijian Cheng, L. Shoemaker
{"title":"Scaling Fuzzy Models","authors":"L. Hall, Dmitry Goldgof, Juana Canul-Reich, Prodip Hore, Weijian Cheng, L. Shoemaker","doi":"10.4018/978-1-60566-858-1.CH002","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH002","url":null,"abstract":"Scaling fuzzy learning systems can be a challenge, because the search space for fuzzy models is larger than that of crisp models. Here, we are concerned with scaling fuzzy systems as the size of the data grows. There are now many collections of data that are terabytes in size and we are moving towards petabyte collections such as a digital Sloan sky survey (Giannella et al., 2006, Gray and Szalay, 2004). AbsTRACT","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130781350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Fuzzy Clustering with Repulsive Prototypes 具有排斥原型的模糊聚类
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-858-1.CH013
F. Rehm, R. Winkler, R. Kruse
{"title":"Fuzzy Clustering with Repulsive Prototypes","authors":"F. Rehm, R. Winkler, R. Kruse","doi":"10.4018/978-1-60566-858-1.CH013","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH013","url":null,"abstract":"A well known issue with prototype-based clustering is the user's obligation to know the right number of clusters in a dataset in advance or to determine it as a part of the data analysis process. There are different approaches to cope with this non-trivial problem. This paper follows the approach to address this problem as an integrated part of the clustering process. An extension to repulsive fuzzy c-means clustering is proposed equipping non-Euclidean prototypes with repulsive properties. Experimental results are presented that demonstrate the feasibility of our technique.","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132802217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Fuzzy Clustering of Large Relational Bioinformatics Datasets 大型关联生物信息学数据集的模糊聚类
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-858-1.CH016
M. Popescu
{"title":"Fuzzy Clustering of Large Relational Bioinformatics Datasets","authors":"M. Popescu","doi":"10.4018/978-1-60566-858-1.CH016","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH016","url":null,"abstract":"","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123971065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
High Scale Fuzzy Video Mining 大规模模糊视频挖掘
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-858-1.CH015
C. Marsala, Marcin Detyniecki
{"title":"High Scale Fuzzy Video Mining","authors":"C. Marsala, Marcin Detyniecki","doi":"10.4018/978-1-60566-858-1.CH015","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH015","url":null,"abstract":"In this chapter, the authors focus on the use of forests of fuzzy decision trees (FFDT) in a video mining application. They discuss how to learn from a high scale video data sets and how to use the trained FFDTs to detect concepts in a high number of video shots. Moreover, the authors study the effect of the size of the forest on the performance; and of the use of fuzzy logic during the classification process. The experiments are performed on a well-know non-video dataset and on a real TV quality video benchmark.","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115697515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Early Warning from Car Warranty Data using a Fuzzy Logic Technique 基于模糊逻辑技术的汽车保修数据预警
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-858-1.CH014
Mark Last, Yael Mendelson, S. Chakrabarty, K. Batra
{"title":"Early Warning from Car Warranty Data using a Fuzzy Logic Technique","authors":"Mark Last, Yael Mendelson, S. Chakrabarty, K. Batra","doi":"10.4018/978-1-60566-858-1.CH014","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH014","url":null,"abstract":"Car manufacturers are interested to detect evolving problems in a car fleet as early as possible so they can take preventive actions and deal with the problems before they become widespread. The vast amount of warranty claims recorded by the car dealers makes the manual process of analyzing this data hardly feasible. This chapter describes a fuzzy-based methodology for automated detection of evolving maintenance problems in massive streams of car warranty data. The empirical distributions of time-to-failure and mileage-to-failure are monitored over time using the advanced, fuzzy approach to comparison of frequency distributions. The authors’ fuzzy-based early warning tool builds upon an automated interpretation of the differences between consecutive histogram plots using a cognitive model of human perception rather than “crisp” statistical models. They demonstrate the effectiveness and the efficiency of the proposed tool on warranty data that is very similar to the actual data gathered from a database within General Motors.","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132412205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Mining Association Rules from Fuzzy DataCubes 从模糊数据库中挖掘关联规则
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-858-1.CH004
Nicolás Marín, C. Molina, D. Sánchez, M. Vila
{"title":"Mining Association Rules from Fuzzy DataCubes","authors":"Nicolás Marín, C. Molina, D. Sánchez, M. Vila","doi":"10.4018/978-1-60566-858-1.CH004","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH004","url":null,"abstract":"The use of online analytical processing (OLAP) systems as data sources for data mining techniques has been widely studied and has resulted in what is known as online analytical mining (OLAM). As a result of both the use of OLAP technology in new fields of knowledge and the merging of data from different sources, it has become necessary for models to support imprecision. We, therefore, need OLAM methods which are able to deal with this imprecision. Association rules are one of the most used data mining techniques. There are several proposals that enable the extraction of association rules on DataCubes but few of these deal with imprecision in the process and give as result complex rule sets. In this chapter the authors will present a method that manages the imprecision and reduces the complexity. They will study the influence of the use of fuzzy logic using different size problems and comparing the results with a crisp approach.","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Linguistic Data Summarization 语言资料汇总
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-858-1.CH008
J. Kacprzyk, S. Zadrożny
{"title":"Linguistic Data Summarization","authors":"J. Kacprzyk, S. Zadrożny","doi":"10.4018/978-1-60566-858-1.CH008","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH008","url":null,"abstract":"We discuss aspects related to the scalability of data mining tools meant in a different way than whether a data mining tool retains its intended functionality as the problem size increases. We introduce a new concept of a cognitive (perceptual) scalability meant as whether as the problem size increases the method remains fully functional in the sense of being able to provide intuitively appealing and comprehensible results to the human user. We argue that the use of natural language in the linguistic data summaries provides a high cognitive (perceptional) scalability because natural language is the only fully natural means of human communication and provides a common language for individuals and groups of different backgrounds, skills, knowledge. We show that the use of Zadeh’s protoform as general representations of linguistic data summaries, proposed by Kacprzyk and Zadrozny (2002; 2005a; 2005b), amplify this advantage leading to an ultimate cognitive (perceptual) scalability.","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121884909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Electronic Hardware for Fuzzy Computation 模糊计算电子硬件
Scalable Fuzzy Algorithms for Data Management and Analysis Pub Date : 1900-01-01 DOI: 10.4018/978-1-60566-858-1.CH001
Koldo Basterretxea, I. D. Campo
{"title":"Electronic Hardware for Fuzzy Computation","authors":"Koldo Basterretxea, I. D. Campo","doi":"10.4018/978-1-60566-858-1.CH001","DOIUrl":"https://doi.org/10.4018/978-1-60566-858-1.CH001","url":null,"abstract":"Electronic hardware development for fuzzy inference-based computing systems (fuzzy hardware) has been an active research area almost since the first papers on successful fuzzy logic applications, mainly fuzzy controllers, were published in the early eighties. Although historically, due to the greater flexibility and compatibility, as well as the advantages and easiness of using high level languages, the majority of fuzzy inference system (FIS) implementations have been software developments to be run on general purpose processors (GPP), only concurrent computation architectures with specific processing units AbsTRACT","PeriodicalId":293388,"journal":{"name":"Scalable Fuzzy Algorithms for Data Management and Analysis","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131454719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
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