Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018053
Shyue-Liang Wang, Chun-Yin Kuo, T. Hong
Data mining of association rules from items in transaction databases has been studied extensively in recent years. In order to discover more practical rules, domain knowledge such as taxonomies of items [9] and similarity among items [11] have been considered to produce generalized association rules and similar association rules respectively. However, these algorithms deal with only transactions with binary values whereas transactions with quantitative values are more commonly seen in real-world applications. This paper thus proposes a new data-mining algorithm for extracting fuzzy knowledge from transactions stored as quantitative values. The proposed algorithm integrates fuzzy set concepts and the a priori mining algorithm to find fuzzy similar association rules in given transaction data sets where similarity relations are assumed among database items. The rules discovered here thus promote coarser granularity of association rules and exhibit quantitative regularity under similarity relations.
{"title":"Mining fuzzy similar association rules from quantitative data","authors":"Shyue-Liang Wang, Chun-Yin Kuo, T. Hong","doi":"10.1109/NAFIPS.2002.1018053","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018053","url":null,"abstract":"Data mining of association rules from items in transaction databases has been studied extensively in recent years. In order to discover more practical rules, domain knowledge such as taxonomies of items [9] and similarity among items [11] have been considered to produce generalized association rules and similar association rules respectively. However, these algorithms deal with only transactions with binary values whereas transactions with quantitative values are more commonly seen in real-world applications. This paper thus proposes a new data-mining algorithm for extracting fuzzy knowledge from transactions stored as quantitative values. The proposed algorithm integrates fuzzy set concepts and the a priori mining algorithm to find fuzzy similar association rules in given transaction data sets where similarity relations are assumed among database items. The rules discovered here thus promote coarser granularity of association rules and exhibit quantitative regularity under similarity relations.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"633 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120978632","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}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018118
Ö. Ciftcioglu
The selection of a set of key fuzzy rules from a given rule base is an important issue for effective fuzzy modeling. For this purpose the clustering and orthogonal transformation methods are the essential tools. The determination of clusters representing fuzzy rules with the consideration of output as well as input spaces is essential. To select orthogonal axes as principal components other than those determined by Gram-Schmidt provides a most compact representation of the input space R/sup p/ with the p premise variables. This approach in principle possesses two important features for fuzzy modeling. On one hand an enhanced effective rule selection, with the consideration of consequence, is obtained. On the other hand substantial computational saving relative to conventional orthogonal-least-squares approach or other conventional clustering methods is achieved.
{"title":"Ordering rules and complexity reduction for fuzzy models","authors":"Ö. Ciftcioglu","doi":"10.1109/NAFIPS.2002.1018118","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018118","url":null,"abstract":"The selection of a set of key fuzzy rules from a given rule base is an important issue for effective fuzzy modeling. For this purpose the clustering and orthogonal transformation methods are the essential tools. The determination of clusters representing fuzzy rules with the consideration of output as well as input spaces is essential. To select orthogonal axes as principal components other than those determined by Gram-Schmidt provides a most compact representation of the input space R/sup p/ with the p premise variables. This approach in principle possesses two important features for fuzzy modeling. On one hand an enhanced effective rule selection, with the consideration of consequence, is obtained. On the other hand substantial computational saving relative to conventional orthogonal-least-squares approach or other conventional clustering methods is achieved.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122618813","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}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018029
M. Berthold, D. E. Patterson, M. Ortolani, H. Hofer, F. Hoppner, O. Callan
We present a variant of fuzzy c-means that allows us to find similar shapes in time series data in a scale-invariant fashion. We use data from protein mass spectrography to show how this approach finds areas of interest without a need for ad-hoc normalizations.
{"title":"Shape-invariant fuzzy clustering of proteomics data","authors":"M. Berthold, D. E. Patterson, M. Ortolani, H. Hofer, F. Hoppner, O. Callan","doi":"10.1109/NAFIPS.2002.1018029","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018029","url":null,"abstract":"We present a variant of fuzzy c-means that allows us to find similar shapes in time series data in a scale-invariant fashion. We use data from protein mass spectrography to show how this approach finds areas of interest without a need for ad-hoc normalizations.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"361 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122794784","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}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018044
J.A. Okivas, P. J. Garcés, F. P. Romero
In this work a concept representation model (FIS-CPU) has been developed, which is based on two types of fuzzy interrelations between words, synonymy and generality, obtained from a fuzzy dictionary of synonyms [1] and a fuzzy ontology respectively. The model has been put into the metasearcher FISS (Fuzzy Interrelations and Synonymy based Searcher) which is able to retrieve groups of web pages conceptually related among them, improving in this way the quality of the search results.
在这项工作中，开发了一个概念表示模型(FIS-CPU)，该模型基于分别从模糊同义词词典[1]和模糊本体中获得的两种模糊词之间的相互关系，即同义词和一般性。该模型已被放入元搜索器FISS (Fuzzy Interrelations and Synonymy based Searcher)中，它能够检索概念上相关的网页组，从而提高搜索结果的质量。
{"title":"FISS: application of fuzzy technologies to an internet metasearcher","authors":"J.A. Okivas, P. J. Garcés, F. P. Romero","doi":"10.1109/NAFIPS.2002.1018044","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018044","url":null,"abstract":"In this work a concept representation model (FIS-CPU) has been developed, which is based on two types of fuzzy interrelations between words, synonymy and generality, obtained from a fuzzy dictionary of synonyms [1] and a fuzzy ontology respectively. The model has been put into the metasearcher FISS (Fuzzy Interrelations and Synonymy based Searcher) which is able to retrieve groups of web pages conceptually related among them, improving in this way the quality of the search results.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"35 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123514251","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}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018043
J. M. Barone, P. Dewan
Over the last few years, several fuzzy-based approaches to linguistic text analysis have suggested that it is possible to find and to use patterns in the surface structure of linguistic utterances (i.e., pragmatic information) which reveal or conform to deeper linguistic regularities or constructs (Le., semantic information). This paper discusses two of these approaches, the fuzzy semantic typing of Subasic and Huettner and the computational semiotics of Rigger. From a theoretical point of view, such methods have been criticized on both philosophical and linguistic grounds. lit this paper, we perform some simple experiments to see whether useful insights or even useful text processing methods can be gleaned from application of techniques derived from one of these pragmatic approaches (computational semiotics). We conclude that the results are inconsistent and provide neither useful surface measures of regularities nor insights into underlying syntactico-semantic properties of linguistic utterance.
{"title":"Can we treat pragmatics like semantics?","authors":"J. M. Barone, P. Dewan","doi":"10.1109/NAFIPS.2002.1018043","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018043","url":null,"abstract":"Over the last few years, several fuzzy-based approaches to linguistic text analysis have suggested that it is possible to find and to use patterns in the surface structure of linguistic utterances (i.e., pragmatic information) which reveal or conform to deeper linguistic regularities or constructs (Le., semantic information). This paper discusses two of these approaches, the fuzzy semantic typing of Subasic and Huettner and the computational semiotics of Rigger. From a theoretical point of view, such methods have been criticized on both philosophical and linguistic grounds. lit this paper, we perform some simple experiments to see whether useful insights or even useful text processing methods can be gleaned from application of techniques derived from one of these pragmatic approaches (computational semiotics). We conclude that the results are inconsistent and provide neither useful surface measures of regularities nor insights into underlying syntactico-semantic properties of linguistic utterance.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"693 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131919416","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}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018103
German Florez, S. Bridges, R. Vaughn
We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection. We describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency. We use sets of fuzzy association rules that are mined from network audit data as models of "normal behavior." To detect anomalous behavior, we generate fuzzy association rules from new audit data and compute the similarity with sets mined from "normal" data. If the similarity values are below a threshold value, an alarm is issued. We describe an algorithm for computing fuzzy association rules based on Borgelt's (2001) prefix trees, modifications to the computation of support and confidence of fuzzy rules, a new method for computing the similarity of two fuzzy rule sets, and feature selection and optimization with genetic algorithms. Experimental results demonstrate that we can achieve better running time and accuracy with these modifications.
{"title":"An improved algorithm for fuzzy data mining for intrusion detection","authors":"German Florez, S. Bridges, R. Vaughn","doi":"10.1109/NAFIPS.2002.1018103","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018103","url":null,"abstract":"We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection. We describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency. We use sets of fuzzy association rules that are mined from network audit data as models of \"normal behavior.\" To detect anomalous behavior, we generate fuzzy association rules from new audit data and compute the similarity with sets mined from \"normal\" data. If the similarity values are below a threshold value, an alarm is issued. We describe an algorithm for computing fuzzy association rules based on Borgelt's (2001) prefix trees, modifications to the computation of support and confidence of fuzzy rules, a new method for computing the similarity of two fuzzy rule sets, and feature selection and optimization with genetic algorithms. Experimental results demonstrate that we can achieve better running time and accuracy with these modifications.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130597191","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}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018086
M. Pandian, R. Nagarajan, S. Yaacob
In this paper, we use the S-curve membership function methodology in a real life industrial problem of mix product selection. This problem occurs in production planning management whereby a decision maker plays an important role in making decision in a fuzzy environment. As an analyst, we try to find a solution which is good enough for the decision maker to make a final decision. We have considered this problem because all the parameters such as objective coefficient, technical coefficient and resource variables are uncertain. This is considered as one of sufficiently large problem involving 29 constraints and 8 variables. A decision maker can identify a performance measure for making a decision that is suitable for achieving satisfactory profit. The decision maker can also suggest to the analyst some possible and practicable changes in fuzzy intervals for improving the satisfactory profit. This interactive process has to go on an on among the analyst, the decision maker and the implementer till an optimum satisfactory decision is achieved and implemented.
{"title":"Fuzzy mix product selection in an interactive production planning","authors":"M. Pandian, R. Nagarajan, S. Yaacob","doi":"10.1109/NAFIPS.2002.1018086","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018086","url":null,"abstract":"In this paper, we use the S-curve membership function methodology in a real life industrial problem of mix product selection. This problem occurs in production planning management whereby a decision maker plays an important role in making decision in a fuzzy environment. As an analyst, we try to find a solution which is good enough for the decision maker to make a final decision. We have considered this problem because all the parameters such as objective coefficient, technical coefficient and resource variables are uncertain. This is considered as one of sufficiently large problem involving 29 constraints and 8 variables. A decision maker can identify a performance measure for making a decision that is suitable for achieving satisfactory profit. The decision maker can also suggest to the analyst some possible and practicable changes in fuzzy intervals for improving the satisfactory profit. This interactive process has to go on an on among the analyst, the decision maker and the implementer till an optimum satisfactory decision is achieved and implemented.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123712485","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}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018085
S. Rubin, R. J. Rush, J. Murthy, M.H. Smith, L. Trajković
This paper describes a shell that has been developed for the purpose of fuzzy qualitative reasoning. The relation among object predicates is defined by object trees that are fully capable of dynamic growth and maintenance. The qualitatively fuzzy inference engine and the developed expert system can then acquire a virtual-rule space that is exponentially (subject to machine implementation constants) larger than the actual, declared-rule space and with a decreasing non-zero likelihood of error. This capability is called knowledge amplification, and the methodology is named KASER. KASER is an acronym for Knowledge Amplification by Structured Expert Randomization. It can handle the knowledge-acquisition bottleneck in expert systems. KASER represents an intelligent, creative system that fails softly, learns over a network, and has enormous potential for automated decision making. KASERs compute with words and phrases and possess capabilities for metaphorical explanations.
{"title":"KASER: a qualitatively fuzzy object-oriented inference engine","authors":"S. Rubin, R. J. Rush, J. Murthy, M.H. Smith, L. Trajković","doi":"10.1109/NAFIPS.2002.1018085","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018085","url":null,"abstract":"This paper describes a shell that has been developed for the purpose of fuzzy qualitative reasoning. The relation among object predicates is defined by object trees that are fully capable of dynamic growth and maintenance. The qualitatively fuzzy inference engine and the developed expert system can then acquire a virtual-rule space that is exponentially (subject to machine implementation constants) larger than the actual, declared-rule space and with a decreasing non-zero likelihood of error. This capability is called knowledge amplification, and the methodology is named KASER. KASER is an acronym for Knowledge Amplification by Structured Expert Randomization. It can handle the knowledge-acquisition bottleneck in expert systems. KASER represents an intelligent, creative system that fails softly, learns over a network, and has enormous potential for automated decision making. KASERs compute with words and phrases and possess capabilities for metaphorical explanations.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116910911","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}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018123
F. Janabi-Sharifi, G. Li
In the steel rolling industry, control of the hot metal rolling process plays an important role in assuring high product quality and safe process operation. Although many advanced looper control technologies for finishing rolling process have emerged, looperless interstand tension control of roughing rolling mills remains a challenging problem. This paper proposes a multistand fuzzy tension control system for a roughing rolling mill. Combined with a novel decoupling strategy, the proposed scheme makes it possible to realize intelligent tension-free control of multiple roughing rolling stands. The results from a virtual rolling test demonstrated the applicability and effectiveness of the proposed technique.
{"title":"Fuzzy multiple stand tension control of a roughing rolling mill","authors":"F. Janabi-Sharifi, G. Li","doi":"10.1109/NAFIPS.2002.1018123","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018123","url":null,"abstract":"In the steel rolling industry, control of the hot metal rolling process plays an important role in assuring high product quality and safe process operation. Although many advanced looper control technologies for finishing rolling process have emerged, looperless interstand tension control of roughing rolling mills remains a challenging problem. This paper proposes a multistand fuzzy tension control system for a roughing rolling mill. Combined with a novel decoupling strategy, the proposed scheme makes it possible to realize intelligent tension-free control of multiple roughing rolling stands. The results from a virtual rolling test demonstrated the applicability and effectiveness of the proposed technique.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126261892","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}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2002.1018105
J. Gomez, D. Dasgupta, O. Nasraoui, F. González
We propose a linear representation scheme for evolving fuzzy rules using the concept of complete binary tree structures. We also use special genetic operators such as gene addition, gene deletion, and variable length crossover. Results show that using these special operators along with the common mutation operator produce useful and minimal structure modifications to the fuzzy expression tree represented by the chromosome. The proposed method (representation and operators) is tested with a number of benchmark data sets including the KDDCup'99 Network Intrusion Detection data.
{"title":"Complete expression trees for evolving fuzzy classifier systems with genetic algorithms and application to network intrusion detection","authors":"J. Gomez, D. Dasgupta, O. Nasraoui, F. González","doi":"10.1109/NAFIPS.2002.1018105","DOIUrl":"https://doi.org/10.1109/NAFIPS.2002.1018105","url":null,"abstract":"We propose a linear representation scheme for evolving fuzzy rules using the concept of complete binary tree structures. We also use special genetic operators such as gene addition, gene deletion, and variable length crossover. Results show that using these special operators along with the common mutation operator produce useful and minimal structure modifications to the fuzzy expression tree represented by the chromosome. The proposed method (representation and operators) is tested with a number of benchmark data sets including the KDDCup'99 Network Intrusion Detection data.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126419939","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}
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