Wojciech Ziarko

Bio: Wojciech Ziarko is an academic researcher from University of Regina. The author has contributed to research in topics: Rough set & Decision table. The author has an hindex of 37, co-authored 105 publications receiving 13929 citations.

Rough sets

Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

Rough sets: probabilistic versus deterministic approach

Abstract: W: B. Gains and J. Boose, editors, Machine Learning and Uncertain Reasoning 3, pages 227-242. Academic Press, New York, NY, 1990. see also: International Journal of Man Machine Studies 29 (1988) 81-85

"Rough Sets, Fuzzy Sets and Knowledge Discovery"

Abstract: An Overview of Knowledge Discovery in Databases: Recent Progress and Challenges.- Rough Sets and Knowledge Discovery: An Overview.- Search for Concepts and Dependencies in Databases.- Rough Sets and Concept Lattices.- Human-Computer Interfaces: DBLEARN and SystemX.- A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARN.- Knowledge Recognition, Rough Sets, and Formal Concept Lattices.- Quantifying Uncertainty of Knowledge Discovered from Databases.- Temporal Rules Discovery Using Datalogic/R+ with Stock Market Data.- A System Architecture for Database Mining Applications.- An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases.- A Rough Set Model for Relational Databases.- Data Filtration: A Rough Set Approach.- Automated Discovery of Empirical Laws in a Science Laboratory.- Hard and Soft Sets.- Rough Set Analysis of Multi-Attribute Decision Problems.- Rough Set Semantics for Non-Classical Logics.- A Note on Categories of Information Systems.- On Rough Sets in Topological Boolean Algebras.- Approximation of Relations.- Variable Precision Rough Sets with Asymmetric Bounds.- Uncertain Reasoning with Interval-Set Algebra.- On a Logic of Information for Reasoning About Knowledge.- Rough Consequence and Rough Algebra.- Formal Description of Rough Sets.- Rough Sets: A Special Case of Interval Structures.- A Pure Logic-Algebraic Analysis of Rough Top and Rough Bottom Equalities.- A Novel Approach to the Minimal Cover Problem.- Algebraic Structures of Rough Sets.- Rough Concept Analysis.- Rough Approximate Operators: Axiomatic Rough Set Theory.- Finding Reducts in Composed Information Systems.- PRIMEROSE: Probabilistic Rule Induction Method Based on Rough Set Theory.- Comparison of Machine Learning and Knowledge Acquisition Methods of Rule Induction Based on Rough Sets.- AQ, Rough Sets, and Matroid Theory.- Rough Classifiers.- A General Two-Stage Approach to Inducing Rules from Examples.- An Incremental Learning Algorithm for Constructing Decision Rules.- Decision Trees for Decision Tables.- Fuzzy Reasoning and Rough Sets.- Fuzzy Representations in Rough Set Approximations.- Trusting an Information Agent.- Handling Various Types of Uncertainty in the Rough Set Approach.- Intelligent Image Filtering Using Rough Sets.- Multilayer Knowledge Base System for Speaker-Independent Recognition of Isolated Words.- Image Segmentation Based on the Indiscernibility Relation.- Accurate Edge Detection Using Rough Sets.- Rough Classification of Pneumonia Patients Using a Clinical Database.- Rough Sets Approach to Analysis of Data of Diagnostic Peritoneal Lavage Applied for Multiple Injuries Patients.- Neural Networks and Rough Sets - Comparison and Combination for Classification of Histological Pictures.- Towards a Parallel Rough Sets Computer.- Learning Conceptual Design Rules: A Rough Sets Approach.- Intelligent Control System Implementation to the Pipe Organ Instrument.- An Implementation of Decomposition Algorithm and its Application in Information Systems Analysis and Logic Synthesis.- ESEP: An Expert System for Environmental Protection.- Author Index.

Data Mining: Concepts and Techniques

Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Term Weighting Approaches in Automatic Text Retrieval

Abstract: The experimental evidence accumulated over the past 20 years indicates that textindexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective term weighting systems. This paper summarizes the insights gained in automatic term weighting, and provides baseline single term indexing models with which other more elaborate content analysis procedures can be compared.

Rough sets

Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.