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56 results on '"Yao, Yiyu"'

7. Three-way clustering: Foundations, survey and challenges.

Author

Wang, Pingxin, Yang, Xibei, Ding, Weiping, Zhan, Jianming, and Yao, Yiyu

Subjects
BIBLIOMETRICS, DATA mining, CLUSTER sampling, CLUSTER analysis (Statistics), RESEARCH & development
Abstract

Clustering, as an unsupervised data mining technique, allows us to classify similar objects into the same cluster according to certain criteria. It helps us identify patterns between objects, reveal the associations between objects, and discover hidden structures. Traditional two-way clustering (2W clustering) algorithms represent one cluster by one set and only two types of relationships are considered between a sample and a cluster, namely, belonging to and not belonging to. Two-way decision is not always feasible especially in situations that are characterized by uncertainty and lack of information. Guided by the principle of three-way decision (3WD) as thinking in threes, three-way clustering (3W clustering) addresses the information uncertainty problem using core and the fringe regions to character a cluster. The universe is split into three sections by these two sets, which capture three kinds of relationships between objects and a cluster, namely, belonging to, partially belonging to, and not belonging-to. Compared with 2W clustering methods, 3W clustering incorporates the fringe region to describe the uncertain relationship between objects and clusters, which provides more information about the clustering structure. This survey points out the historical developments of three-way clustering and makes an overview of the achievements in the field of three-way clustering. In addition, to reap a clearer grasp of the development and research significance of three-way clustering, we divide the existing three-way clustering approaches into two categories and present the bibliometric analysis of related approaches. Finally, we point out some challenges and future research topics in three-way clustering. It is hoped that this review can serve as a reference and provide convenience for scholars and practitioners in the field of three-way clustering. • The foundations of three-way clustering are clarified. • A bibliometric analysis is conducted to clarify research status and trends. • The contributions and deficiencies of existing researches are summarized. • The potential challenges and future research topics of three-way clustering are put forward. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Three-way decision perspectives on class-specific attribute reducts.

Author

Ma, Xi-Ao and Yao, Yiyu

Subjects
*ROUGH sets, *BOUNDARY value problems, *PROBABILITY theory, *DECISION logic tables, *DATA mining
Abstract

In rough set theory, a decision class (i.e., a subset of objects) is approximated by three pair-wise disjoint positive, boundary, and negative regions. The concept of three-way decisions is introduced to provide a new interpretation of the three regions. We construct acceptance, non-commitment, and rejection rules, respectively, from the positive, boundary, and negative regions. The notion of class-specific attribute reducts concerns a minimal set of attributes used in constructing such rules. Existing studies on class-specific attribute reducts only consider the positive region and hence only the acceptance rules. In many situations such as medical diagnosis, we are also interested in negative rules or rule-out rules. This motivates the present study on three-way decision perspectives on class-specific attribute reducts. In addition to positive-region based attribute reducts, we study negative-region and positive-and-negative-region based attribute reducts. We investigate relationships among the three types of reducts. Although the three types of reducts are equivalent in consistent decision tables, they are not equivalent in inconsistent decision tables. By extending the framework, we study the three types of class-specific attribute reducts in probabilistic rough set models and their relationships. Finally, we give a general definition of class-specific attribute reducts. [ABSTRACT FROM AUTHOR]

Published
2018
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9. LOCAL PECULIARITY ORIENTED DATA MINING AND ITS APPLICATION IN OUTLIER DETECTION.

Author

YANG, JIAN, ZHONG, NING, YAO, YIYU, and WANG, JUE

Subjects
CASE studies, DATA mining, GAUSSIAN distribution, DATABASE searching, DECISION support systems
Abstract

Peculiarity oriented mining (POM), aimed at discovering peculiarity rules hidden in a dataset, is a data mining method. Peculiarity factor (PF) is one of the most important concepts in POM. In this paper, it is proved that PF can accurately characterize the peculiarity of data sampled from a normal distribution. However, for a general one-dimensional distribution, it does not have the property. A local version of PF, called LPF, is proposed to solve the difficulty. LPF can effectively describe the peculiarity of data sampled from a continuous one-dimensional distribution. Based on LPF, a framework of local peculiarity oriented mining is presented, which consists of two steps, namely, peculiar data identification and peculiar data analysis. Two algorithms for peculiar data identification and a case study of peculiar data analysis are given to make the framework practical. Experiments on several benchmark datasets show their good performance. [ABSTRACT FROM AUTHOR]

Published
2012
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10. Probabilistic rule induction with the LERS data mining system.

Author

Grzymala-Busse, Jerzy W. and Yao, Yiyu

Subjects
PROBABILISTIC automata, DATA mining, ROUGH sets, APPROXIMATION theory, BOUNDARY value problems, ALGORITHMS, SET theory
Abstract

Based on classical rough set approximations, the LERS (Learning from Examples based on Rough Sets) data mining system induces two types of rules, namely, certain rules from lower approximations and possible rules from upper approximations. By relaxing the stringent requirement of the classical rough sets, one can obtain probabilistic approximations. The LERS can be easily applied to induce probabilistic positive and boundary rules from probabilistic positive and boundary regions. This paper discusses several fundamental issues related to probabilistic rule induction with LERS, including rule induction algorithm, quantitative measures associated with rules, and the rule conflict resolution method. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Published
2011
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11. Semi-supervised Rough Cost/Benefit Decisions.

Author

Grzymala-Busse, Jerzy W., Yao, Yiyu, Ziarko, Wojciech, Lingras, Pawan, Chen, Min, and Miao, Duoqian

Subjects
*SUPERVISED learning, *COST analysis, *DATA mining, *PROFIT & loss, *DECISION making, *CLASSIFICATION, *SALES promotion
Abstract

Most of the business decisions are based on cost and benefit considerations. Data mining techniques that make it possible for the businesses to incorporate financial considerations will be moremeaningful to the decisionmakers. Decision theoretic framework has been helpful in providing a better understanding of classification models. This study describes a semi-supervised decision theoretic rough set model. The model is based on an extension of decision theoretic model proposed by Yao. The proposal is used to model financial cost/benefit scenarios for a promotional campaign in a real-world retail store. [ABSTRACT FROM AUTHOR]

Published
2009

12. An Incremental Approach for Inducing Knowledge from Dynamic Information Systems.

Author

Grzymala-Busse, Jerzy W., Yao, Yiyu, Ziarko, Wojciech, Liu, Dun, Li, Tianrui, Ruan, Da, and Zou, Weili

Subjects
*INFORMATION theory, *DYNAMICS, *SET theory, *CASE studies, *FEASIBILITY studies, *MATHEMATICAL analysis, *DATA mining
Abstract

Knowledge in an information system evolves with its dynamical environment. A new concept of interesting knowledge based on both accuracy and coverage is defined in this paper for dynamic information systems. An incremental model and approach as well as its algorithm for inducing interesting knowledge are proposed when the object set varies over time. A case study validates the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2009

13. An Integration of Cloud Transform and Rough Set Theory to Induction of Decision Trees.

Author

Grzymala-Busse, Jerzy W., Yao, Yiyu, Ziarko, Wojciech, Song, Jing, Li, Tianrui, and Ruan, Da

Subjects
*NUMERICAL integration, *MATHEMATICAL transformations, *ROUGH sets, *DECISION trees, *DATA mining, *CLOUD computing, *CLASSIFICATION, *ALGORITHMS
Abstract

Decision trees are one of the most popular data-mining techniques for knowledge discovery. Many approaches for induction of decision trees often deal with the continuous data and missing values in information systems. However, they do not perform well in real situations. This paper presents a new algorithm, decision tree construction based on the Cloud transform and Rough set theory under the characteristic relation (CR), for mining classification knowledge from a given data set. The continuous data is transformed into discrete qualitative concepts via the cloud transformation and then the attribute with the smallest weighted mean roughness under the characteristic relation is selected as the current splitting node. Experimental evaluation shows the decision trees constructed by the CR algorithm tend to have a simpler structure, much higher classification accuracy and more understandable rules than those by C5.0 in most cases. [ABSTRACT FROM AUTHOR]

Published
2009

14. Relational peculiarity-oriented mining.

Author

Ohshima, Muneaki, Zhong, Ning, Yao, YiYu, and Liu, Chunnian

Subjects
DATA mining, RELATIONAL databases, COMPUTER programming, RESEARCH methodology, IDENTIFICATION, EXPERIMENTS
Abstract

Peculiarity rules are a new type of useful knowledge that can be discovered by searching the relevance among peculiar data. A main task in mining such knowledge is peculiarity identification. Previous methods for finding peculiar data focus on attribute values. By extending to record-level peculiarity, this paper investigates relational peculiarity-oriented mining. Peculiarity rules are mined, and more importantly explained, in a relational mining framework. Several experiments are carried out and the results show that relational peculiarity-oriented mining is effective. [ABSTRACT FROM AUTHOR]

Published
2007
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15. Fundamentals of Knowledge Technology.

Author

Grzymala-Busse, Jerzy W., Yao, Yiyu, and Ziarko, Wojciech

Subjects
*INFORMATION technology, *DATA mining, *ROUGH sets, *APPROXIMATION theory, *NUMERICAL analysis, *COST analysis, *MONOTONIC functions
Published
2009
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16. Concept-cognitive learning survey: Mining and fusing knowledge from data.

Author

Guo, Doudou, Xu, Weihua, Ding, Weiping, Yao, Yiyu, Wang, Xizhao, Pedrycz, Witold, and Qian, Yuhua

Subjects
*COGNITIVE learning, *ARTIFICIAL intelligence, *COGNITIVE computing, *EVIDENCE gaps, *GRANULAR computing
Abstract

Concept-cognitive learning (CCL), an emerging intelligence learning paradigm, has recently become a popular research subject in artificial intelligence and cognitive computing. A central notion of CCL is cognitive and learning things via concepts. In this process, concepts play a fundamental role when mining and fusing knowledge from data to wisdom. With the in-depth research and expansion of CCL in scopes, goals, and methodologies, some difficulties have gradually emerged, including some vague terminology, ambiguous views, and scattered research. Hence, a systematic and comprehensive review of the development process and advanced research about CCL is particularly necessary at the moment. This paper summarizes the theoretical significance, application value, and future development potential of CCL. More importantly, by synthesizing the reviewed related research, we can acquire some interesting results and answer three essential questions: (1) why examine a cognitive and learning framework based on concept? (2) what is the concept-cognitive learning? (3) how to make concept-cognitive learning? The findings of this work could act as a valuable guide for related studies in quest of a clear understanding of the closely related research issues around concept-cognitive learning. • It is the first paper that attempts to provide an in-depth analysis of the advancement of CCL. • It is a multi-view categorization of CCL in research scopes, goals, and methodologies. • It is an elucidation of the main research gaps and suggestions for the related study of CCL. • It acquires some interesting results by synthesizing the reviewed related research of CCL. [ABSTRACT FROM AUTHOR]

Published
2024
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17. A semantically sound approach to Pawlak rough sets and covering-based rough sets.

Author

D'eer, Lynn, Cornelis, Chris, and Yao, Yiyu

Subjects
*ROUGH sets, *SEMANTIC computing, *BOOLEAN algebra, *DATA mining
Abstract

In this paper, we discuss a semantically sound approach to covering-based rough sets. We recall and elaborate on a conceptual approach to Pawlak's rough set model, in which we consider a two-part descriptive language. The first part of the language is used to describe conjunctive concepts, while in the second part disjunctions are allowed as well. Given the language, we discuss its elementary and definable sets, and we study how the approximation operators can be seen as derived notions of the family of definable sets, which is represented by a Boolean algebra over a partition. Furthermore, we generalize the two parts of the language in order to describe concepts of covering-based rough sets. Unfortunately, the family of definable sets will no longer be represented by a Boolean algebra over a partition, but by the union-closure of a covering. Therefore, only the derived covering-based lower approximations of sets are definable for the generalized language. In addition, it is discussed how the two-part languages are used to construct decision rules, which are used in data mining and machine learning. [ABSTRACT FROM AUTHOR]

Published
2016
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18. Automatically Determining the Popularity of a Song

Author

Chiru, Costin, Popescu, Oana-Georgiana, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Polkowski, Lech, editor, Yao, Yiyu, editor, Artiemjew, Piotr, editor, Ciucci, Davide, editor, Liu, Dun, editor, Ślęzak, Dominik, editor, and Zielosko, Beata, editor

Published
2017
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21. Chinese Medicine Formula Network Analysis for Core Herbal Discovery

Author

Yuan, Gao, Zheng, Liu, Chong-Jun, Wang, Xin-Sheng, Fan, Jun-Yuan, Xie, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Zanzotto, Fabio Massimo, editor, Tsumoto, Shusaku, editor, Taatgen, Niels, editor, and Yao, Yiyu, editor

Published
2012
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22. Granule Oriented Data Warehouse Model

Author

Wu, Jingtong, Li, Yuefeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Wen, Peng, editor, Li, Yuefeng, editor, Polkowski, Lech, editor, Yao, Yiyu, editor, Tsumoto, Shusaku, editor, and Wang, Guoyin, editor

Published
2009
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23. Multiagent Framework for Bio-data Mining

Author

Yang, Pengyi, Tao, Li, Xu, Liang, Zhang, Zili, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Wen, Peng, editor, Li, Yuefeng, editor, Polkowski, Lech, editor, Yao, Yiyu, editor, Tsumoto, Shusaku, editor, and Wang, Guoyin, editor

Published
2009
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24. Development of the Data Preprocessing Agent’s Knowledge for Data Mining Using Rough Set Theory

Author

Othman, Zulaiha Ali, Bakar, Azuraliza Abu, Othman, Zalinda, Rosli, Suzanna, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Wen, Peng, editor, Li, Yuefeng, editor, Polkowski, Lech, editor, Yao, Yiyu, editor, Tsumoto, Shusaku, editor, and Wang, Guoyin, editor

Published
2009
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25. Reasoning Decision Rules of an Uncertain System

Author

Zeng, Huanglin, Zeng, Xiaohui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Wen, Peng, editor, Li, Yuefeng, editor, Polkowski, Lech, editor, Yao, Yiyu, editor, Tsumoto, Shusaku, editor, and Wang, Guoyin, editor

Published
2009
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26. Online Granular Prediction Model for Web Prefetching

Author

Gu, Zhimin, Ban, Zhijie, Zhang, Hongli, Duan, Zhaolei, Ren, Xiaojin, Carbonell, Jaime G., editor, Siekmann, J\'org, editor, Wang, Guoyin, editor, Li, Tianrui, editor, Grzymala-Busse, Jerzy W., editor, Miao, Duoqian, editor, Skowron, Andrzej, editor, and Yao, Yiyu, editor

Published
2008
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32. The Research of Sampling for Mining Frequent Itemsets

Author

Hu, Xuegang, Yu, Haitao, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Guo-Ying, editor, Peters, James F., editor, Skowron, Andrzej, editor, and Yao, Yiyu, editor

Published
2006
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33. A Comparison of Three Graph Partitioning Based Methods for Consensus Clustering

Author

Hu, Tianming, Zhao, Weiquan, Wang, Xiaoqiang, Li, Zhixiong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Guo-Ying, editor, Peters, James F., editor, Skowron, Andrzej, editor, and Yao, Yiyu, editor

Published
2006
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34. Developing a Rule Evaluation Support Method Based on Objective Indices

Author

Abe, Hidenao, Tsumoto, Shusaku, Ohsaki, Miho, Yamaguchi, Takahira, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Guo-Ying, editor, Peters, James F., editor, Skowron, Andrzej, editor, and Yao, Yiyu, editor

Published
2006
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35. Classification Rule Mining Based on Particle Swarm Optimization

Author

Wang, Ziqiang, Sun, Xia, Zhang, Dexian, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Guo-Ying, editor, Peters, James F., editor, Skowron, Andrzej, editor, and Yao, Yiyu, editor

Published
2006
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36. Advances in the Quotient Space Theory and Its Applications

Author

Zhao, Li-Quan, Zhang, Ling, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Guo-Ying, editor, Peters, James F., editor, Skowron, Andrzej, editor, and Yao, Yiyu, editor

Published
2006
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37. Axiomatic Systems of Generalized Rough Sets

Author

Zhu, William, Wang, Feiyue, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Guo-Ying, editor, Peters, James F., editor, Skowron, Andrzej, editor, and Yao, Yiyu, editor

Published
2006
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38. FADS: A Fuzzy Anomaly Detection System

Author

Li, Dan, Wang, Kefei, Deogun, Jitender S., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Guo-Ying, editor, Peters, James F., editor, Skowron, Andrzej, editor, and Yao, Yiyu, editor

Published
2006
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43. On Data Mining for Direct Marketing

Author

Ou, Chuangxin, Liu, Chunnian, Huang, Jiajing, Zhong, Ning, Carbonell, J. G., editor, Siekmann, J., editor, Wang, Guoyin, editor, Liu, Qing, editor, Yao, Yiyu, editor, and Skowron, Andrzej, editor

Published
2003
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