Data mining in deductive databases using query flocks

Data mining can be defined as a process for finding trends and patterns in large data. An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. Most research on data mining is concentrated on traditional relational data model. On the other hand, the query flocks technique, which extends the concept of association rule mining with a 'generate-and-test' model for different kind of patterns, can also be applied to deductive databases. In this paper, query flocks technique is extended with view definitions including recursive views. Although in our system query flock technique can be applied to a data base schema including both the intensional data base (IDB) or rules and the extensible data base (EDB) or tabled relations, we have designed an architecture to compile query flocks from datalog into SQL in order to be able to use commercially available data base management systems (DBMS) as an underlying engine of our system. However, since recursive datalog views (IDB's) cannot be converted directly into SQL statements, they are materialized before the final compilation operation. On this architecture, optimizations suitable for the extended query flocks are also introduced. Using,the prototype system, which is developed on a commercial database environment, advantages of the new architecture together with the optimizations, are also presented.


ILP-based concept discovery in multi-relational data mining
Kavurucu, Yusuf; Karagöz, Pınar; Toroslu, İsmail Hakkı (Elsevier BV, 2009-11-01)
Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, an ILP-based concept discov...
An approach to the mean shift outlier model by Tikhonov regularization and conic programming
TAYLAN, PAKİZE; Yerlikaya-Oezkurt, Fatma; Weber, Gerhard Wilhelm (IOS Press, 2014-01-01)
In statistical research, regression models based on data play a central role; one of these models is the linear regression model. However, this model may give misleading results when data contain outliers. The outliers in linear regression can be resolved in two stages: by using the Mean Shift Outlier Model (MSOM) and by providing a new solution for this model. First, we construct a Tikhonov regularization problem for the MSOM. Then, we treat this problem using convex optimization techniques, specifically c...
Semantic data modeling of spatiotemporal database applications
Yazıcı, Adnan; Sun, N (Wiley, 2001-07-01)
Due to the ubiquity of space-related and time-related information, the ability of a database system to deal with both spatial and temporal phenomenon facts in a spatiotemporal applications is highly desired. However, uncertain and fuzzy information in these applications highly increases the complexity of database modeling. In this paper we introduce a semantic data modeling approach for spatiotemporal database applications. We specifically focus on various aspects of spatial and temporal database issues and...
Domain adaptation on graphs by learning graph topologies: theoretical analysis and an algorithm
Vural, Elif (The Scientific and Technological Research Council of Turkey, 2019-01-01)
Traditional machine learning algorithms assume that the training and test data have the same distribution, while this assumption does not necessarily hold in real applications. Domain adaptation methods take into account the deviations in data distribution. In this work, we study the problem of domain adaptation on graphs. We consider a source graph and a target graph constructed with samples drawn from data manifolds. We study the problem of estimating the unknown class labels on the target graph using the...
Repetition support and mining cyclic patterns
Toroslu, İsmail Hakkı (Elsevier BV, 2003-10-01)
For customer transaction database, the support (customer support) of the sequential pattern is defined as the fraction of the customers supporting the sequence. We define new forms of the mining patterns, called cyclic patterns, as an extension to the sequential pattern mining by introducing a new parameter, the repetition support. For customer transaction database, the repetition support specifies the minimum number of repetitions of the patterns in each customer transaction sequence. Repeated patterns can...
Citation Formats
İ. H. Toroslu, “Data mining in deductive databases using query flocks,” EXPERT SYSTEMS WITH APPLICATIONS, pp. 395–407, 2005, Accessed: 00, 2020. [Online]. Available: