In this paper, we propose an unsupervised method for discovering inference rules from text, such as “X is author of Y ≈ X wrote Y”, “X solved Y ≈ X found a solution to Y”, and “X caused Y ≈ Y is triggered by X”. Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an e...

This paper presents an autonomous algorithm for discovering exception rules from data sets. An exception rule, which is defined as a deviational pattern to a well-known fact, exhibits unexpectedness and is sometimes extremely useful in spite of its obscurity. Previous discovery approaches for this type of knowledge have neglected the problem of evalnating the reliability of the rules extracted ...

A number of techniques have been developed to turn data into useful knowledge. Most of the algorithms in data mining find association rules among transactions using binary values and at single concept level. However it will be more exciting to discover hierarchical association rules for decision makers. In this work we have integrated association rule mining with fuzzy set theory and hierarchy....

MOTIVATION Even in a simple organism like yeast Saccharomyces cerevisiae, transcription is an extremely complex process. The expression of sets of genes can be turned on or off by the binding of specific transcription factors to the promoter regions of genes. Experimental and computational approaches have been proposed to establish mappings of DNA-binding locations of transcription factors. How...

Software maintenance consumes a large amount of its total life cycle costs. In fact, maintainers spend a lot of time analyzing source code, configurations and resource definitions referring to the documentation in order to gain a deeper understanding of the logic of business rules implemented in the system. To facilitate these activities, we propose a model-driven approach on business rules dis...

The focus of this paper is the discovery of negative association rules. Such association rules are complementary to the sorts of association rules most often encountered in literatures and have the forms of X→¬Y or ¬X→Y. We present a rule discovery algorithm that finds a useful subset of valid negative rules. In generating negative rules, we employ a hierarchical graph-structured taxonomy of do...

In this research study, we analyze the performance of bio inspired classification approaches by selecting Ant-Miners (Ant-Miner, cAnt_Miner, cAnt_Miner2 and cAnt_MinerPB) for the discovery of classification rules in terms of accuracy, terms per rule, number of rules, running time and model size discovered by the corresponding rule mining algorithm. Classification rule discovery is still a chall...

A problem of association rules discovery in a multivariate time series is considered in this paper. A method for finding interpretable association rules between frequent qualitative patterns is proposed. A pattern is defined as a sequence of mixed states. The multivariate time series is transformed into a set of labeled intervals and mined for frequently occurring patterns. Then these patterns ...

This paper presents results, at an early stage of research work, of the use of fuzzy decision trees in a multimedia framework. We present the discovery of rules in three different indexing scenarios. These rules represent knowledge that can be interpreted as guidelines for the development of better indexing tools. We use a fuzzy decision tree algorithm to extract these rules (just) from color p...

Data mining is a technique for discovering useful information from large databases. This technique is currently being profitably used by a number of industries. A common approach for information discovery is to identify association rules which reveal relationships among different items. In this paper, we use this approach to analyse a large database containing medical-record data. Our aim is to...