Reductions for Frequency-Based Data Mining Problems

Studying the computational complexity of problems is one of the - if not the - fundamental questions in computer science. Yet, surprisingly little is known about the computational complexity of many central problems in data mining. In this paper we study frequency-based problems and propose a new type of reduction that allows us to compare the complexities of the maximal frequent pattern mining problems in different domains (e.g. graphs or sequences). Our results extend those of Kimelfeld and Kolaitis [ACM TODS, 2014] to a broader range of data mining problems. Our results show that, by allowing constraints in the pattern space, the complexities of many maximal frequent pattern mining problems collapse. These problems include maximal frequent subgraphs in labelled graphs, maximal frequent itemsets, and maximal frequent subsequences with no repetitions. In addition to theoretical interest, our results might yield more efficient algorithms for the studied problems.Comment: This is an extended version of a paper of the same title to appear in the Proceedings of the 17th IEEE International Conference on Data Mining (ICDM'17

A framework for visualizing association mining results

Association mining is one of the most used data mining tech- niques due to interpretable and actionable results. In this study we pro- pose a framework to visualize the association mining results, specifically frequent itemsets and association rules, as graphs. We demonstrate the applicability and usefulness of our approach through a Market Basket Analysis (MBA) case study where we visually explore the data mining results for a supermarket data set. In this case study we derive several interesting insights regarding the relationships among the items and sug- gest how they can be used as basis for decision making in retailing

Applications of concurrent access patterns in web usage mining

This paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns (CAP), where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access pat-terns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with real-world datasets highlight the applicability of the approach in web navigation

SemGrAM - Integrating semantic graphs into association rule mining

To date, most association rule mining algorithms have assumed that the domains of items are either discrete or, in a limited number of cases, hierarchical, categorical or linear. This constrains the search for interesting rules to those that satisfy the specified quality metrics as independent values or as higher level concepts of those values. However, in many cases the determination of a single hierarchy is not practicable and, for many datasets, an item’s value may be taken from a domain that is more conveniently structured as a graph with weights indicating semantic (or conceptual) distance. Research in the development of algorithms that generate disjunctive association rules has allowed the production of rules such as Radios V TVs -> Cables. In many cases there is little semantic relationship between the disjunctive terms and arguably less readable rules such as Radios V Tuesday -> Cables can result. This paper describes two association rule mining algorithms, SemGrAMG and SemGrAMP, that accommodate conceptual distance information contained in a semantic graph. The SemGrAM algorithms permit the discovery of rules that include an association between sets of cognate groups of item values. The paper discusses the algorithms, the design decisions made during their development and some experimental results.Sydney, NS