Mining association rules is a popular and well researched method for discovering interesting relations between variables in large databases. A practical problem is that at medium to low support values often a large number of frequent itemsets and an even larger number of association rules are found in a database. A widely used approach is to gradually increase minimum support and minimum confid...

Data mining queries are often submitted concurrently to the data mining system. The data mining system should take advantage of overlapping of the mined datasets. In this paper we focus on frequent itemset mining and we discuss and experimentally evaluate the implementation of the Common Counting method on top of the Apriori algorithm. The general idea of Common Counting is to reduce the number...

Genetic algorithm is a search technique purely based on natural evolution process. It is widely used by the data mining community for classification rule discovery in complex domains. During the learning process it makes several passes over the data set for determining the accuracy of the potential rules. Due to this characteristic it becomes an extremely I/O intensive slow process. It is parti...

Privacy preserving data mining is a novel research direction in data mining and statistical databases, which has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. There have been two types of privacy proposed concerning data mining. The first type of privacy, called output privacy, is that the data is altered s...

We propose a method that processes raw informal medical texts (from health forums) and formal texts (outpatient records) in Bulgarian language in order to extract typical word co-occurrences in the form of association rules. When mining these rules we use some context information and small terminological lexicons to generalize the extracted frequent patterns. This allows to study informal expre...

Assessment of discovered patterns is an important issue in the field of knowledge discovery. This paper presents an evaluation method that utilizes citation (reference) information to assess the quality of discovered document relations. With the concept of transitivity as direct/indirect citations, a series of evaluation criteria is introduced to define the validity of discovered relations. Two...

In recent times, the development of privacy technologies has promoted the speed of research on privacy-preserving collaborative data mining. People borrowed the ideas of secure multi-party computation and developed secure multi-party protocols to deal with privacy-preserving collaborative data mining problems. Random perturbation was also identified to be an efficient estimation technique to so...

Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. To encourage users to provide correct inputs, we recently proposed a data distortion scheme for association rule mining that simultaneously provides both privacy to the user and accuracy in the mining results. However, mining the distorte...

Frequent itemset mining is an essential part of data analysis and data mining. Recent works propose interesting SAT-based encodings for the problem of discovering frequent itemsets. Our aim in this work is to define strategies for adapting SAT solvers to such encodings in order to improve models enumeration. In this context, we deeply study the effects of restart, branching heuristics and claus...

Motivated by a growing need for intelligent housing to accommodate aging populations, we propose a novel application of intertransaction association rule (IAR) mining to detect anomalous behaviour in smart home occupants. An efficient mining algorithm that avoids the candidate generation bottleneck limiting the application of current IAR mining algorithms on smart home data sets is detailed. An...