Towards a semantic and statistical selection of association rules

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. Association rules selection is a classical topic to address this issue, yet, new innovated approaches are required in order to provide help to decision makers. Hence, many interesting- ness measures have been defined to statistically evaluate and filter the association rules. However, these measures present two major problems. On the one hand, they do not allow eliminating irrelevant rules, on the other hand, their abun- dance leads to the heterogeneity of the evaluation results which leads to confusion in decision making. In this paper, we propose a two-winged approach to select statistically in- teresting and semantically incomparable rules. Our statis- tical selection helps discovering interesting association rules without favoring or excluding any measure. The semantic comparability helps to decide if the considered association rules are semantically related i.e comparable. The outcomes of our experiments on real datasets show promising results in terms of reduction in the number of rules

Semantics-based classification of rule interestingness measures

Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the subject, the scope, and the nature of the measure. These criteria seem essential to grasp the meaning of the measures, and therefore to help the user to choose the ones (s)he wants to apply. Moreover, the classification allows one to compare the rules to closely related concepts such as similarities, implications, and equivalences. Finally, the classification shows that some interesting combinations of the criteria are not satisfied by any index

Novel Algorithms for Cross-Ontology Multi-Level Data Mining

The wide spread use of ontologies in many scientific areas creates a wealth of ontologyannotated data and necessitates the development of ontology-based data mining algorithms. We have developed generalization and mining algorithms for discovering cross-ontology relationships via ontology-based data mining. We present new interestingness measures to evaluate the discovered cross-ontology relationships. The methods presented in this dissertation employ generalization as an ontology traversal technique for the discovery of interesting and informative relationships at multiple levels of abstraction between concepts from different ontologies. The generalization algorithms combine ontological annotations with the structure and semantics of the ontologies themselves to discover interesting crossontology relationships. The first algorithm uses the depth of ontological concepts as a guide for generalization. The ontology annotations are translated to higher levels of abstraction one level at a time accompanied by incremental association rule mining. The second algorithm conducts a generalization of ontology terms to all their ancestors via transitive ontology relations and then mines cross-ontology multi-level association rules from the generalized transactions. Our interestingness measures use implicit knowledge conveyed by the relation semantics of the ontologies to capture the usefulness of cross-ontology relationships. We describe the use of information theoretic metrics to capture the interestingness of cross-ontology relationships and the specificity of ontology terms with respect to an annotation dataset. Our generalization and data mining agorithms are applied to the Gene Ontology and the postnatal Mouse Anatomy Ontology. The results presented in this work demonstrate that our generalization algorithms and interestingness measures discover more interesting and better quality relationships than approaches that do not use generalization. Our algorithms can be used by researchers and ontology developers to discover inter-ontology connections. Additionally, the cross-ontology relationships discovered using our algorithms can be used by researchers to understand different aspects of entities that interest them

Irrelevant feature and rule removal for structural associative classification

In the classification task, the presence of irrelevant features can significantly degrade the performance of classification algorithms,in terms of additional processing time, more complex models and the likelihood that the models have poor generalization power due to the over fitting problem.Practical applications of association rule mining often suffer from overwhelming number of rules that are generated, many of which are not interesting or not useful for the application in question.Removing rules comprised of irrelevant features can significantly improve the overall performance.In this paper, we explore and compare the use of a feature selection measure to filter out unnecessary and irrelevant features/attributes prior to association rules generation.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data items.Empirical results confirm that by utilizing feature subset selection prior to association rule generation, a large number of rules with irrelevant features can be eliminated.More importantly, the results reveal that removing rules that hold irrelevant features improve the accuracy rate and capability to retain the rule coverage rate of structural associative association

Measuring Interestingness – Perspectives on Anomaly Detection

We live in a data deluge. Our ability to gather, distribute, and store information has grown immensely over the past two decades. With this overabundance of data, the core knowledge discovery problem is no longer in the gathering of this data, but rather in the retrieving of relevant data efficiently. While the most common approach is to use rule interestingness to filter results of the association rule generation process, study of literature suggests that interestingness is difficult to define quantitatively and is best summarized as, “a record or pattern is interesting if it suggests a change in an established model.” In this paper we elaborate on the term interestingness, and the surrounding taxonomy of interestingness measures, anomalies, novelty and surprisingness. We review and summarize the current state of literature surrounding interestingness and associated approaches. Keywords: Interestingness, anomaly detection, rare-class mining, Interestingness measures, outliers, surprisingness, novelt

Forensic Data Mining: Finding Intrusion Patterns in Evidentiary Data

In The extensive growth of computing networks and tools and tricks for intruding into and attacking networks has underscored the importance of intrusion detection in network security. Yet, contemporary intrusion detection systems (IDS) are limiting in that they typically employ a misuse detection strategy, with searches for patterns of program or user behavior that match known intrusion scenarios, or signatures. Accordingly, there is a need for more robust and adaptive methods for designing and updating intrusion detection systems. One promising approach is the use of data mining methods for discovering intrusion patterns. Discovered patterns and profiles can be translated into classifiers for detecting deviations from normal usage patterns. Among promising mining methods are association rules, link analysis, and rule-induction algorithms. Our particular contribution is a unique approach to combining association rules with link analysis and a rule-induction algorithm to augment intrusion detection systems

Interactive visual exploration of association rules with rule-focusing methodology

International audienceOn account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge for decision making, the user (a decision maker specialized in the data studied) needs to rummage through the rules. To assist him/her in this task, we here propose the rule-focusing methodology, an interactive methodology for the visual post-processing of association rules. It allows the user to explore large sets of rules freely by focusing his/her attention on limited subsets. This new approach relies on rule interestingness measures, on a visual representation, and on interactive navigation among the rules. We have implemented the rule-focusing methodology in a prototype system called ARVis. It exploits the user's focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm