Zbigniew W. Ras

Foundations of Intelligent Systems

Invited Papers.- Methodologies for Automated Telephone Answering.- Anomaly Detection in Computer Security and an Application to File System Accesses.- Regular Papers.- A Machine Text-Inspired Machine Learning Approach for Identification of Transmembrane Helix Boundaries.- Visualization of Similarities and Dissimilarities in Rules Using Multidimensional Scaling.- Learning Profiles Based on Hierarchical Hidden Markov Model.- Statistical Independence from the Viewpoint of Linear Algebra.- Bitmap Index-Based Decision Trees.- On Automatic Modeling and Use of Domain-Specific Ontologies.- Using Self-Adaptable Probes for Dynamic Parameter Control of Parallel Evolutionary Algorithms.- Robust Inference of Bayesian Networks Using Speciated Evolution and Ensemble.- Mining the Semantic Web: A Logic-Based Methodology.- Analysis of Textual Data with Multiple Classes.- SARM - Succinct Association Rule Mining: An Approach to Enhance Association Mining.- Learning the Daily Model of Network Traffic.- ARGUS: Rete + DBMS = Efficient Persistent Profile Matching on Large-Volume Data Streams.- Failing Queries in Distributed Autonomous Information System.- Evaluation of Two Systems on Multi-class Multi-label Document Classification.- Uncertain Knowledge Gathering: An Evolutionary Approach.- Duality in Knowledge Compilation Techniques.- Data Protection in Distributed Database Systems.- A Softened Formulation of Inductive Learning and Its Use for Coronary Disease Data.- Subsystem Based Generalizations of Rough Set Approximations.- Model-Based Cluster Analysis for Web Users Sessions.- On Autonomous k-Means Clustering.- CSI: Clustered Segment Indexing for Efficient Approximate Searching on the Secondary Structure of Protein Sequences.- Using Supervised Clustering to Enhance Classifiers.- Modelling Good Entry Pages on the Web.- A Query Expression and Processing Technique for an XML Search Engine.- Adapting the Object Role Modelling Method for Ontology Modelling.- Identifying Content Blocks from Web Documents.- Building the Data Warehouse of Frequent Itemsets in the DWFIST Approach.- Normal Forms for Knowledge Compilation.- On the Approximate Division of Fuzzy Relations.- Efficient Learning of Pseudo-Boolean Functions from Limited Training Data.- Discovering Partial Periodic Sequential Association Rules with Time Lag in Multiple Sequences for Prediction.- Mining and Filtering Multi-level Spatial Association Rules with ARES.- Association Reducts: A Framework for Mining Multi-attribute Dependencies.- Frequent Pattern Mining with Preferences-Utility Functions Approach.- Semantic-Based Access to Digital Document Databases.- Statistical Database Modeling for Privacy Preserving Database Generation.- Scalable Inductive Learning on Partitioned Data.- Agent-Based Home Simulation and Control.- Aggregates and Preferences in Logic Programming.- The Chisholm Paradox and the Situation Calculus.- A Logic Approach for LTL System Modification.- Anticipatory Agents Based on Anticipatory Reasoning.- Extracting Emotions from Music Data.- An Intelligent System for Assisting Elderly People.- Multi-strategy Instance Selection in Mining Chronic Hepatitis Data.- A Probabilistic Approach to Finding Geometric Objects in Spatial Datasets of the Milky Way.- Towards Ad-Hoc Rule Semantics for Gene Expression Data.- Flexible Pattern Discovery with (Extended) Disjunctive Logic Programming.- Interactive SOM-Based Gene Grouping: An Approach to Gene Expression Data Analysis.- Some Theoretical Properties of Mutual Information for Student Assessments in Intelligent Tutoring Systems.- Cooperative Query Answering for RDF.- Intelligent Information Retrieval for Web-Based Design Data Repository.- Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms.- A Distance-Based Algorithm for Clustering Database User Sessions.- User-Interest-Based Document Filtering via Semi-supervised Clustering.- A Filter Feature Selection Method for Clustering.- Automatic Determination of the Number of Fuzzy Clusters Using Simulated Annealing with Variable Representation.- Experimental Analysis of the Q-Matrix Method in Knowledge Discovery.- Clustering Time-Series Medical Databases Based on the Improved Multiscale Matching.- Efficient Causal Interaction Learning with Applications in Microarray.- A Dynamic Adaptive Sampling Algorithm (DASA) for Real World Applications: Finger Print Recognition and Face Recognition.- Catching the Picospams.- Personalized Peer Filtering for a Dynamic Information Push.- Getting Computers to See Information Graphics So Users Do Not Have to.- A Data Model Based on Paraconsistent Intuitionistic Fuzzy Relations.- Logical Data Independence Reconsidered (Extended Abstract).- Estimation of the Density of Datasets with Decision Diagrams.

Action-Rules: How to Increase Profit of a Company

Decision tables classifying customers into groups of different profitability are used for mining rules classifying customers. Attributes are divided into two groups: stable and flexible. By stable attributes we mean attributes which values can not be changed by a bank (age, marital status, number of children are the examples). On the other hand attributes (like percentage rate or loan approval to buy a house in certain area) which values can be changed or influenced by a bank are called flexible. Rules are extracted from a decision table given preference to flexible attributes. This new class of rules forms a special repository of rules from which new rules called actionrules are constructed. They show what actions should be taken to improve the profitability of customers.

The wisdom web : new challenges for web intelligence (WI)

The World Wide Web (WWW) has profoundly changed our ways of doing things, from business and communication to entertainment and learning. This impact is inevitable due to the facts that the Web connectivity rapidly increases and that the on-line information astronomically explodes. In order to not only live with such a change but also benefit from the information infrastructure that WWW has empowered, we have witnessed the fast development as well as application of various WWW technologies, which cover:

Association Action Rules

Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Previous research on action rule discovery usually required the extraction of classification rules before constructing any action rule. This paper gives anew approach for generating association-type action rules. The notion of frequent action sets and Apriori-like strategy generating them is proposed. We introduce the notion of a representative action rules and give an algorithm to construct them directly from frequent action sets. Finally, we introduce the notion of a simple association action rule, the cost of association action rule, and give a strategy to construct simple association action rules of a lowest cost.

Discovering Extended Action-Rules (System DEAR)

Action rules introduced in [3] and investigated further in [4] assume that attributes in a database are divided into two groups: stable and flexible. In general, an action rule can be constructed from two rules extracted earlier from the same database. Furthermore, we assume that these two rules describe two different decision classes and that our goal is to re-classify some objects from one of these decision classes to the other one. Flexible attributes provide a tool for making hints to a business user what changes within some values of flexible attributes are needed. for a given object to re-classify this object to another decision class. In [3], we suggested what changes are needed to classification attributes listed in both rules but we did not consider situations when such an attribute is listed only in one of these rules. Also, neither in [3] nor [4] we provide a way to compute support and confidence of action rules. ! In this paper, we show how system DEAR is discovering extended action rules which give better strategies for re-classifying objects than strategies provided by action rules. Also, the confidence of extended action rules is much higher than confidence of corresponding action rules. System DEAR, implemented in KDD Laboratory at UNC-Charlotte, requires Windows 95 or higher. It does not discretize numerical attributes which means some discretization algorithm has to applied before DEAR is used

Query approximate answering system for an incomplete DKBS

A Distributed Knowledge-Based System (DKBS) is a collection of autonomous knowledge-based systems called agents which are capable of interacting with each other. A query can be submitted to one agent or a group of agents. An agent when contacted by the user acts as a master agent. If he is unable to answer the query, he looks for help from other agents which act as his slaves. In this paper, an agent is represented by an information system (either complete or incomplete), a collection of rules called a knowledge base and the Query Approximate Answering System (QAAS). Rules are interpreted as descriptions of some attribute values in terms of other attribute values. These descriptions are usually not precise and they only provide a lower approximation of attribute values. We say that an attribute value is reachable by an agent if either it belongs to the domain of one of the attributes in his information system or it is a decision part of one of the rules in his knowledge base. In the second case, we assume that all attribute values from the classification part of a rule have to be reachable. When rules are discovered by one site of DKBS which currently acts as a slave, they are sent to the master agent of that slave. The QAAS of the master agent will use these rules to answer a query submitted by the user and next it will store these rules in the agents knowledge base. So, the set of reachable attribute values at any site of DKBS is constantly changing. Knowledge bases built that way might easily become inconsistent because rules they contain are created independently at different sites of DKBS. The problem of repairing inconsistent rules was investigated in [19]. In this paper, we propose a strategy for discovering rules in incomplete information systems and give a formal system for handling queries in DKBS where each site contains either an incomplete or a complete information system.

Advances in Music Information Retrieval

Sound waves propagate through various media, and allow communication or entertainment for us, humans. Music we hear or create can be perceived in such aspects as rhythm, melody, harmony, timbre, or mood. All these elements of music can be of interest for users of music information retrieval systems. Since vast music repositories are available for everyone in everyday use (both in private collections, and in the Internet), it is desirable and becomes necessary to browse music collections by contents. Therefore, music information retrieval can be potentially of interest for every user of computers and the Internet. There is a lot of research performed in music information retrieval domain, and the outcomes, as well as trends in this research, are certainly worth popularizing. This idea motivated us to prepare the book on Advances in Music Information Retrieval. It is divided into four sections: MIR Methods and Platforms, Harmony, Music Similarity, and Content Based Identification and Retrieval. Glossary of basic terms is given at the end of the book, to familiarize readers with vocabulary referring to music information retrieval.

Mining for interesting action rules

In this paper, we give a strategy for constructing all action rules from a given information system and show that action rules constructed by system DEAR, cover only a small part of all action rules. Clearly, we are not interested in all action rules as we are not interested in extracting all possible rules from an information system. Classical strategies like See5, LERS, CART, Rosetta, Weka discover rules whose classification part is either the shortest or close to the shortest. This approach basically rules out all other classification rules unless they are surprising rules. In this paper, we introduce the notion of cost of an action rule and define interesting action rules as rules of the smallest cost. We give a strategy showing how interesting action rules can be generated from action rules discovered by system DEAR.

Analysis of Sound Features for Music Timbre Recognition

Recently, communication, digital music creation, and computer storage technology has led to the dynamic increasing of online music repositories in both number and size, where automatic content-based indexing is critical for users to identify possible favorite music pieces. Timbre recognition is one of the important subtasks for such an indexing purpose. Lots of research has been carried out in exploring new sound features to describe the characteristics of a musical sound. The moving picture expert group (MPEG) provides a standard set of multimedia features, including low level acoustical features based on latest research in this area. This paper introduces our newly designed temporal features used for automatic indexing of musical sounds and evaluates them with MPEG7 descriptors, and other popular features.

Advances in Intelligent Information Systems

Intelligent Information Systems (IIS) can be defined as the next generation of Information Systems (IS) developed as a result of integration of AI and database (DB) technologies. IIS embody knowledge that allows them to exhibit intelligent behavior, allows them to cooperate with users and other systems in problem solving, discovery, retrieval, and manipulation of data and knowledge. For any IIS to serve its purpose, the information must be available when it is needed. This means that the computing systems used to store data and process the information, and the security controls used to protect it must be functioning correctly. This book covers some of the above topics and it is divided into four sections: Classification, Approximation and Data Security, Knowledge Management, and Application of IIS to medical and music domains.