56,994 research outputs found
A fuzzy approach for mining quantitative association rules
During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to find association rules in databases with quantitative attributes. The algorithms usually discretize the attribute domains into sharp intervals, and then apply simpler algorithms developed for boolean attributes. An example of a quantitative association rule might be "10% of married people between age 50 and 70 have at least 2 cars". Recently, fuzzy sets were suggested to represent intervals with non-sharp boundaries. Using the fuzzy concept, the above example could be rephrased e.g. "10% of married old people have several cars". However, if the fuzzy sets are not well chosen, anomalies may occur. In this paper we tackle this problem by introducing an additional fuzzy normalization process. Then we present the definition of quantitative association rules based on fuzzy set theory and propose a new algorithm for mining fuzzy association rules. The algorithm uses generalized definitions for interest measures. Experimental results show the efficiency of the algorithm for large databases
The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques
The paper describes VEX-93 as a hybrid environment for developing
knowledge-based and problem solver systems. It integrates methods and
techniques from artificial intelligence, image and signal processing and
data analysis, which can be mixed. Two hierarchical levels of reasoning
contains an intelligent toolbox with one upper strategic inference engine
and four lower ones containing specific reasoning models: truth-functional
(rule-based), probabilistic (causal networks), fuzzy (rule-based) and
case-based (frames). There are image/signal processing-analysis capabilities
in the form of programming languages with more than one hundred primitive
functions.
User-made programs are embeddable within knowledge basis, allowing the
combination of perception and reasoning. The data analyzer toolbox contains
a collection of numerical classification, pattern recognition and ordination
methods, with neural network tools and a data base query language at
inference engines's disposal.
VEX-93 is an open system able to communicate with external computer programs
relevant to a particular application. Metaknowledge can be used for
elaborate conclusions, and man-machine interaction includes, besides windows
and graphical interfaces, acceptance of voice commands and production of
speech output.
The system was conceived for real-world applications in general domains, but
an example of a concrete medical diagnostic support system at present under
completion as a cuban-spanish project is mentioned.
Present version of VEX-93 is a huge system composed by about one and half
millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version
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Evaluating aggregate functions on possibilistic data
The need for extending information management systems to handle the imprecision of information found in the real world has been recognized. Fuzzy set theory together with possibility theory represent a uniform framework for extending the relational database model with these features. However, none of the existing proposals for handling imprecision in the literature has dealt with queries involving a functional evaluation of a set of items, traditionally referred to as aggregation. Two kinds of aggregate operators, namely, scalar aggregates and aggregate functions, exist. Both are important for most real-world applications, and are thus being supported by traditional languages like SQL or QUEL. This paper presents a framework for handling these two types of aggregates in the context of imprecise information. We consider three cases, specifically, aggregates within vague queries on precise data, aggregates within precisely specified queries on possibilistic data, and aggregates within vague queries on imprecise data. These extensions are based on fuzzy set-theoretical concepts such as the extension principle, the sigma-count operation, and the possibilistic expected value. The consistency and completeness of the proposed operations is shown
Implementing imperfect information in fuzzy databases
Information in real-world applications is often
vague, imprecise and uncertain. Ignoring the inherent imperfect
nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several
substantial information, which may be very useful in several
data-intensive applications. In database context, several fuzzy
database models have been proposed. In these works, fuzziness
is introduced at different levels. Common to all these proposals is
the support of fuzziness at the attribute level. This paper proposes
first a rich set of data types devoted to model the different kinds
of imperfect information. The paper then proposes a formal
approach to implement these data types. The proposed approach
was implemented within a relational object database model but it
is generic enough to be incorporated into other database models.ou
FARS: Fuzzy Ant based Recommender System for Web Users
Recommender systems are useful tools which provide an
adaptive web environment for web users. Nowadays, having a
user friendly website is a big challenge in e-commerce
technology. In this paper, applying the benefits of both
collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on
collaborative behavior of ants (FARS). FARS works in two
phases: modeling and recommendation. First, user’s behaviors
are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations
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