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.).

Machine learning

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Data Mining - Concepts and Techniques.

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Modeling Survival Data Extending The Cox Model

Matrix Factorization Techniques for Recommender Systems

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

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Effective Pattern Discovery for Text Mining

Practical machine learning algorithms are known to degrade in performance (prediction accuracy) when faced with many features (sometimes attribute is used instead of feature) that are not necessary for rule discovery. To cope with this problem, many methods for selecting a subset of features have been proposed. Among such methods, the filter approach that selects a feature subset using a preprocessing step, and the wrapper approach that selects an optimal feature subset from the space of possible subsets of features using the induction algorithm itself as a part of the evaluation function, are two typical ones. Although the filter approach is a faster one, it has some blindness and the performance of induction is not considered. On the other hand, the optimal feature subsets can be obtained by using the wrapper approach, but it is not easy to use because of the complexity of time and space. In this paper, we propose an algorithm which is using rough set theory with greedy heuristics for feature selection. Selecting features is similar to the filter approach, but the evaluation criterion is related to the performance of induction. That is, we select the features that do not damage the performance of induction.

Using Rough Sets with Heuristics for Feature Selection

Invited Talks.- Decision Rules, Bayes' Rule and Rough Sets.- A New Direction in System Analysis: From Computation with Measurements to Computation with Perceptions.- On Text Mining Techniques for Personalization.- A Road to Discovery Science.- Rough Computing: Foundations and Applications.- Calculi of Granules Based on Rough Set Theory: Approximate Distributed Synthesis and Granular Semantics for Computing with Words.- Discovery of Rules about Complications.- Rough Genetic Algorithms.- Classifying Faults in High Voltage Power Systems: A Rough-Fuzzy Neural Computational Approach.- Rough Set Theory and Its Applications.- Toward Spatial Reasoning in the Framework of Rough Mereology.- An Algorithm for Finding Equivalence Relations from Tables with Non-deterministic Information.- On the Extension of Rough Sets under Incomplete Information.- On Rough Relations: An Alternative Formulation.- Formal Rough Concept Analysis.- Noise Reduction in Telecommunication Channels Using Rough Sets and Neural Networks.- Rough Set Analysis of Electrostimulation Test Database for the Prediction of Post-Operative Profits in Cochlear Implanted Patients.- A Rough Set-Based Approach to Text Classification.- Modular Rough Fuzzy MLP: Evolutionary Design.- Approximate Reducts and Association Rules.- Handling Missing Values in Rough Set Analysis of Multi-attribute and Multi-criteria Decision Problems.- The Generic Rough Set Inductive Logic Programming Model and Motifs in Strings.- Rough Problem Settings for Inductive Logic Programming.- Using Rough Sets with Heuristics for Feature Selection.- The Discretization of Continuous Attributes Based on Compatibility Rough Set and Genetic Algorithm.- Fuzzy Set Theory and Its Applications.- Level Cut Conditioning Approach to the Necessity Measure Specification.- Four c-Regression Methods and Classification Functions.- Context-Free Fuzzy Sets in Data Mining Context.- Applying Fuzzy Hypothesis Testing to Medical Data.- Generating a Macroeconomic Fuzzy Forecasting System Using Evolutionary Search.- Fuzzy Control of Nonlinear Systems Using Nonlinearized Parameterization.- Control of Chaotic Systems Using Fuzzy Model-Based Regulators.- Fuzzy Behavior-Based Control for the Obstacle Avoidance of Multi-link Manipulators.- Fuzzy Future Value and Annual Cash Flow Analyses.- Semi-linear Equation with Fuzzy Parameters.- Non-classical Logic and Approximate Reasoning.- First Order Rough Logic-Revisited.- A Generalized Decision Logic in Interval-Set-Valued Information Tables.- Many-Valued Dynamic Logic for Qualitative Decision Theory.- Incorporating Fuzzy Set Theory and Matrix Logic in Multi-layer Logic.- Fuzzy Logic as Interfacing Media for Constraint Propagation Based on Theories of Chu Space and Information Flow.- Pattern Reasoning: A New Solution for Knowledge Acquisition Problem.- Probabilistic Inference and Bayesian Theorem Based on Logical Implication.- Reasoning with Neural Logic Networks.- The Resolution for Rough Propositional Logic with Lower (L) and Upper (H) Approximate Operators.- Information Granulation and Granular Computing.- Information Granules in Distributed Environment.- Evolving Granules for Classification for Discovering Difference in the Usage of Words.- Interval Evaluation by AHP with Rough Set Concept.- Interval Density Functions in Conflict Analysis.- Incorporating Personal Database Unification by Granular Computing.- Data Mining and Knowledge Discovery.- Knowledge-Driven Discovery of Operational Definitions.- A Closest Fit Approach to Missing Attribute Values in Preterm Birth Data.- Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density.- A Distance-Based Clustering and Selection of Association Rules on Numeric Attributes.- Knowledge Discovery for Protein Tertiary Substructures.- Integrating Classification and Association Rule Mining: A Concept Lattice Framework.- Using Rough Genetic and Kohonen's Neural Network for Conceptual Cluster Discovery in Data Mining.- Towards Automated Optimal Equity Portfolios Discovery in a Knowledge Sharing Financial Data Warehouse.- Rule-Evolver: An Evolutionary Approach for Data Mining.- Machine Learning.- Decision Making with Probabilistic Decision Tables.- The Iterated Version Space Learning.- An Empirical Study on Rule Quality Measures.- Rules as Attributes in Classifier Construction.- An Algorithm to Find the Optimized Network Structure in an Incremental Learning.- Patterns in Numerical Data: Practical Approximations to Kolmogorov Complexity.- Performance Prediction for Classification Systems.- Intelligent Agents and Systems.- Flexible Optimization and Evolution of Underwater Autonomous Agents.- Ontology-Based Multi-agent Model of an Information Security System.- Optimal Multi-scale Time Series Decomposition for Financial Forecasting Using Wavelet Thresholding Techniques.- Computerized Spelling Recognition of Words Expressed in the Sound Approach.- An Adaptive Handwriting Recognition System.

New directions in rough sets, data mining, and granular-soft computing, 7th International Workshop, RSFDGrC'99, Yamaguchi, Japan, November 9-11, 1999 : proceedings

In this paper, we analyze quantitative measures associated with if-then type rules. Basic quantities are identified and many existing measures are examined using the basic quantities. The main objective is to provide a synthesis of existing results in a simple and unified framework. The quantitative measure is viewed as a multi-facet concept, representing the confidence, uncertainty, applicability, quality, accuracy, and interestingness of rules. Roughly, they may be classified as representing one-way and two-way supports.

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An Analysis of Quantitative Measures Associated with Rules

Peculiarity rules are a new class of rules which can be discovered by searching relevance among a relatively small number of peculiar data. Peculiarity oriented mining in multiple data sources is different from, and complementary to, existing approaches for discovering new, surprising, and interesting patterns hidden in data. A theoretical framework for peculiarity oriented mining is presented. Within the proposed framework, we give a formal interpretation and comparison of three classes of rules, namely, association rules, exception rules, and peculiarity rules, as well as describe how to mine interesting peculiarity rules in multiple databases.

Ning Zhong

Papers

Effective Pattern Discovery for Text Mining

Using Rough Sets with Heuristics for Feature Selection

New directions in rough sets, data mining, and granular-soft computing, 7th International Workshop, RSFDGrC'99, Yamaguchi, Japan, November 9-11, 1999 : proceedings

An Analysis of Quantitative Measures Associated with Rules

Peculiarity oriented multidatabase mining