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Tutorial 3. Clustering Techniques for Large data Sets - From the Past to the Future 5

Tutorial 3. Clustering Techniques for Large Data Sets - From...

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5th ACM SIGKDD international conference on Knowledge Discovery and data mining, KDD 1999

作者： Keim, Daniel A. Hinneburg, Alexander University of Munich Germany University of Halle Germany

ISBN: (纸本)1581131712

Because of the fast technological progress, the amount of information which is stored in databases is rapidly increasing. In addition, new applications require the storage and retrieval of complex multimedia objects which are often represented by high-dimensional feature vectors. Finding the valuable information hidden in those databases is a difficult task. Cluster analysis is one of the basic techniques which is often applied in analyzing large data sets. Originating from the area of statistics, most cluster analysis algorithms have originally been developed for relatively small data sets. In the recent years, the clustering algorithms have been extended to efficiently work on large data sets, and some of them even allow the clustering of high-dimensional feature vectors. Many such methods use some kind of an index structure for an efficient retrieval of the required data;other approaches are based on preprocessing for a more efficient clustering. the main goal of the tutorial is to provide an overview of the state-of-the-Art in cluster discovery methods for large databases, covering well-known clustering methods from related fields such as statistics, pattern recognition, and machine learning, as well as database techniques which allow them to work efficiently on large databases. the target audience of the tutorial are researchers and practitioners from statistics, databases, and machine learning, who are interested in the state-of-the art of cluster discovery methods and their applications to large databases. the tutorial especially addresses people from academia who are interested in developing new cluster discovery algorithms, and people from industry who want to apply cluster discovery methods in analyzing large databases. the tutorial is structured as follows: First, we give a brief motivation for clustering from modern data mining applications. We discuss important design decisions and explain the interdependencies with the properties of data. We then intro

关键词： Cluster analysis

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A problem of selecting optimal subset of fuzzy-valued features

A problem of selecting optimal subset of fuzzy-valued featur...

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IEEE international conference on Systems, Man and Cybernetics

作者： X.Z. Wang E.C.C. Tsang D.S. Yeung Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China

Feature subset selection refers to a data mining enhancement technique which aims to reduce the number of features to be used. this reduction is expected to improve the performance of data mining algorithms to be used, in aspects of speed, accuracy and simplicity. Although there has been some work on feature subset selection, research into the theoretically computational complexity of this problem and on the optimal selection of fuzzy-valued feature subsets has not been carried out. this paper focuses on a problem called optimal fuzzy-valued feature subset selection (OFFSS) which is regarded as being important but difficult in machine learning and pattern recognition. the measure of the quality of a set of features is defined by the overall overlapping degree between two classes of examples and the size of feature subset. the main contributions of this paper are that: (1) the concept of fuzzy extension matrix is introduced; (2) the computational complexity of OFFSS is proved to be NP-hard; (3) a simple but powerful heuristic algorithm for OFFSS is given; and (4) the feasibility and simplicity of the proposed algorithm are demonstrated via applications of OFFSS to input selection of neuro-fuzzy systems and to fuzzy decision tree induction.

关键词： data mining Computational complexity pattern recognition Size measurement Uncertainty machine learning Fuzzy systems Electronic mail Heuristic algorithms Fuzzy neural networks

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learning to Predict Rare Events in Event Sequences 4

Learning to Predict Rare Events in Event Sequences

引用

4th international conference on Knowledge Discovery and data mining, KDD 1998

作者： Weiss, Gary M. Hirsh, Haym Department of Computer Science Rutgers University New BrunswickNJ08903 United States

ISBN: (纸本)1577350707

learning to predict rare events from sequences of events with categorical features is an important, real-world, problem that existing statistical and machine learning methods are not well suited to solve. this paper describes timeweaver, a genetic algorithm based machine learning system that predicts rare events by identifying predictive temporal and sequential patterns. Timeweaver is applied to the task of predicting telecommunication equipment failures from 110,000 alarm messages and is shown to outperform existing learning methods. © 1998 Proceedings of the 4th international conference on Knowledge Discovery and data mining, KDD 1998. All rights reserved.

关键词： Genetic algorithms

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learning to Predict the Duration of an Automobile Trip 4

Learning to Predict the Duration of an Automobile Trip

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4th international conference on Knowledge Discovery and data mining, KDD 1998

作者： Handley, Simon Langley, Pat Rauscher, Folke A. Daimler-Benz Research and Technology Center 1510 Page Mill Rd. Palo AltoCA94304 United States Daimler-Benz AG Research & Technology - FT3/KL P.O. Box 2360 UlmD-89013 Germany

ISBN: (纸本)1577350707

In this paper, we explore the use of machine learning and data mining to improve the prediction of travel times in an automobile. We consider two formulations of this problem, one that involves predicting speeds at different stages along the route and another that relies on direct prediction of transit time. We focus on the second formulation, which we apply to data collected from the San Diego freeway system. We report experiments on these data with fc-nećirest neighbour combined with a wrapper to select useful features and normalization parameters. the results suggest that 3-nearest neighbour, when using information from freeway sensors, substantially outperforms predictions av£iilable from existing digital maps. Analyses also reveal some surprises about the usefulness of other features like the time émd day of the trip. © 1998 Proceedings of the 4th international conference on Knowledge Discovery and data mining, KDD 1998. All rights reserved.

关键词： Travel time

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Towards the Personalization of Algorithms Evaluation in data mining 4

Towards the Personalization of Algorithms Evaluation in Data...

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4th international conference on Knowledge Discovery and data mining, KDD 1998

作者： Nakhaeizadeh, Gholamreza Schnabl, Alexander Daimler-Benz AG Research and Technology 3 P.O. Box 23 60 UlmD-89013 Germany Technical University Vienna Department of Econometrics Operations Research and Systems Theory Argentinierstr. 8/1 ViennaA-1040 Austria

ISBN: (纸本)1577350707

Like model selection in statistics, the choice of appropriate data mining Algorithms (DM-Algorithms) is a very important task in the process of Knowledge Discovery. Due to this fact it is necessary to have sophisticated metrics that can be used as comparators to evaluate alternative DMatgorithms. It has been shown in literature, that data Envelopment Analysis (DEA) is an appropriate platform to develop multi-criteria evaluation metrics that can consider — in contrary to mono-criteria metrics - all positive and negative properties of DM-algorithms. We discuss different extensions of DEA that enable consideration of qualitative properties of DM-algorithms and consideration of users preferences in development of evaluation metrics. the results open new discussions in the general debate on model selection in statistics and machine learning. © 1998 Proceedings of the 4th international conference on Knowledge Discovery and data mining, KDD 1998. All rights reserved.

关键词： data envelopment analysis

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A data mining Support Environment and its Application on Insurance data 4

A Data Mining Support Environment and its Application on Ins...

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4th international conference on Knowledge Discovery and data mining, KDD 1998

作者： Staudt, M. Kietz, J.-U. Reimer, U. ZurichCH-8022 Switzerland

ISBN: (纸本)1577350707

Huge masses of digital data about products, customers and competitors have become available for companies in the services sector. In order to exploit its inherent (and often hidden) knowledge for improving business processes the application of data mining technology is the only way for reaching good and efficient results, as opposed to purely manual and interactive data exploration. this paper reports on a project initiated at Swiss Life for mining its data resources from the life insurance business. Based on the data Warehouse MASY collecting all relevant data from the OLTP systems for the processing of private life insurance contracts, a data mining environment is set up which integrates a palette of tools for automatic data analysis, in particular machine learning approaches. Special emphasis lies on establishing comfortable data preprocessing support for normalised relational databases and on the management of meta data. Copyright © 1998, American Association for Artificial Intelligence (***). All rights reserved.

关键词： data mining

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Targeting Business Users with Decision Table Classifiers 4

Targeting Business Users with Decision Table Classifiers

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4th international conference on Knowledge Discovery and data mining, KDD 1998

作者： Kohavi, Ron Sommerfield, Daniel Data Mining and Visualization Silicon Graphics Inc. 2011 N. Shoreline Blvd Mountain ViewCA94043-1389 United States

ISBN: (纸本)1577350707

Business users and analysts commonly use spreadsheets and 2D plots to analyze and understand their data. On-line Analytical Processing (OLAP) provides these users with added flexibility in pivoting data around different attributes and drilling up and down the multi-dimensional cube of aggregations. machine learning researchers, however, have concentrated on hypothesis spaces that are foreign to most users: hyperplanes (Perceptrons), neural networks, Bayesian networks, decision trees, nearest neighbors, etc. In this paper we advocate the use of decision table classifiers that are easy for line-of-business users to understand. We describe several variants of algorithms for learning decision tables, compare their performance, and describe a visualization mechanism that we have implemented in MineSet. the performance of decision tables is comparable to other known algorithms, such as C4.5/C5.0, yet the resulting classifiers use fewer attributes and are more comprehensible. © 1998 Proceedings of the 4th international conference on Knowledge Discovery and data mining, KDD 1998. All rights reserved.

关键词： Decision tables

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4th international Colloquium on Grammatical Inference, ICGI 1998

4th International Colloquium on Grammatical Inference, ICGI ...

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4th international Colloquium on Grammatical Inference, ICGI 1998

ISBN: (纸本)3540647767

the proceedings contain 23 papers. the special focus in this conference is on Grammatical Inference. the topics include: Results of the abbadingo one DFA learning competition and a new evidence-driven state merging algorithm;learning k-variable pattern languages efficiently stochastically finite on average from positive data;meaning helps learning syntax;a polynomial time incremental algorithm for learning DFA;the data driven approach applied to the OSTIA algorithm;grammar model and grammar induction in the system NL PAGE;approximate learning of random subsequential transducers;learning stochastic finite automata from experts;learning deterministic finite automaton with a recurrent neural network;applying grammatical inference in learning a language model for oral dialogue;real language learning;a stochastic search approach to grammar induction;transducer-learning experiments on language understanding;locally threshold testable languages in strict sense;learning a subclass of linear languages from positive structural information;grammatical inference in document recognition;stochastic inference of regular tree languages;how considering incompatible state mergings may reduce the DFA induction search tree;learning regular grammars to model musical style;learning a subclass of context-free languages;using symbol clustering to improve probabilistic automaton inference and a performance evaluation of automatic survey classifiers;pattern discovery in biosequences.

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An inductive logic programming query language for database mining (extended abstract) 4th

An inductive logic programming query language for database m...

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4th international conference on Artificial Intelligence and Symbolic Computation, AISC 1998

作者： De Raedt, Luc Department of Computer Science Katholieke Universiteit Leuven Celestijnenlaan 200A HeverleeB-3001 Belgium

ISBN: (纸本)3540649603

First, a short introduction to inductive logic programming and machine learning is presented and then an inductive database mining query language RDM (Relational database mining language). RDM integrates concepts from inductive logic programming, constraint logic programming, deductive databases and meta-programming into a flexible environment for relational knowledge discovery in databases. the approach is motivated by the view of data mining as a querying process(see Imielinkski and Mannila, CACM 96). Because the primitives of the presented query language can easily be combined with the ?rolog programming language, complex systems and behaviour can be specified declaratively. Integrating a database mining querying language with principles of inductive logic programming has the added benefit that it becomes feasible to search for regularities involving multiple relations in a database. the proposal for an inductive logic programming query language puts inductive logic programming into a new perspective © Springer-Verlag Berlin Heidelberg 1998.

关键词： data mining

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Finding frequent substructures in chemical compounds 4

Finding frequent substructures in chemical compounds

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4th international conference on Knowledge Discovery and data mining, KDD 1998

作者： Dehaspe, Luc Toivonen, Hannu King, Ross Donald Dept. of Computer Science Katholieke Universiteit Leuven Celestijnenlaan 200A HeverleeB-3001 Belgium Rolf Nevanlinna Institute Dept. of Computer Science University of Helsinki P.O. Box 4 FIN-00014 Finland Dept. of Computer Science The Univ. of Wales Aberystwyth Penglais Ceredigion AberystwythSY23 3DB United Kingdom

ISBN: (纸本)1577350707

the discovery of the relationships between chemical structure and biological function is central to biological science and medicine. In this paper we apply data mining to the problem of predicting chemical carcinogenicity. this toxicology application was launched at IJCAI'97 as a research challenge for artificial intelligence. Our approach to the problem is descriptive rather than based on classification;the goal being to find common substructures and properties in chemical compounds, and in this way to contribute to scientific insight. this approach contrasts with previous machine learning research on this problem, which has mainly concentrated on predicting the toxicity of unknown chemicals. Our contribution to the field of data mining is the ability to discover useful frequent patterns that are beyond the complexity of association rules or their known variants. this is vital to the problem, which requires the discovery of patterns that are out of the reach of simple transformations to frequent itemsets. We present a knowledge discovery method for structured data, where patterns reflect the one-to-many and many-to-many relationships of several tables. Background knowledge, represented in a uniform manner in some of the tables, has an essential role here, unlike in most data mining settings for the discovery of frequent patterns. Copyright © 1998, American Association for Artificial Intelligence (***). All rights reserved.

关键词： data mining

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