Constrained pattern mining in the new era

Abstract

Twenty years of research on frequent itemset mining, or pattern mining, has led to the existence of a set of efficient algorithms for identifying different types of patterns, from transactional to sequential. Despite the great advances in this field, big data brought a completely new context to operate, with new challenges arising from the growth in data size, dynamics and complexity. These challenges include the shift not only from static to dynamic data, but also from tabular to complex data sources, such as social networks (expressed as graphs) and data warehouses (expressed as multi-relational models). In this new context, and more than ever, users need effective ways to control the large number of discovered patterns, and to be able to choose what patterns to consider at each time. The most accepted and common approach to minimize these drawbacks has been to capture and represent the semantics of the domain through constraints, and use them not only to reduce the number of results, but also to focus the algorithms in areas where it is more likely to gain information and return more interesting results. The use of constraints in pattern mining has been widely studied, and there are a lot of proposed types of constraints and pushing strategies. In this paper, we present a new global view of the work done on the incorporation of constraints in the pattern mining process. In particular, we propose a new framework for constrained pattern mining, that allows us to organize and analyze existing algorithms and strategies, based on the different types and properties of constraints, and on the data sources they are able to handle.

Practical guidelines for change recommendation using association rule mining

“…In the rest of this Section, we distinguish related work on these three aspects, focusing on the area of software maintenance and evolution. Parameters in Association Rule Mining: In general, association rule mining algorithms differ from each other in the data structures used to represent transactions, and the strategy used to select transactions relevant to a given query [37]. However, the majority of such algorithms are characterized by similar parameters.…”

Construction productivity fuzzy knowledge base management system

“…Despite the prevalence of KM systems in data mining and pattern finding, the use of historical data is still lacking. Therefore, the models build with the current data systems do not represent the knowledge in its entirety that would be essential for predicting construction productivity (Soibelman and Kim 2002;Soibelman et al 2004;Reffat et al 2006;and Mougel et al 2010;Guns 2016;and Silva and Antunes 2016).…”