A framework for automated association mining over multiple databases

Literature on association mining, the data mining methodology that investigates associations between items, has primarily focused on efficiently mining larger databases. The motivation for association mining is to use the rules obtained from historical data to influence future transactions. However, associations in transactional processes change significantly over time, implying that rules extracted for a given time interval may not be applicable for a later time interval. Hence, an analysis framework is necessary to identify how associations change over time. This paper presents such a framework, reports the implementation of the framework as a tool, and demonstrates the applicability of and the necessity for the framework through a case study in the domain of finance

The smallest set of constraints that explains the data : a randomization approach

Randomization methods can be used to assess statistical significance of data mining results. A randomization method typically consists of a sampler which draws data sets from a null distribution, and a test statistic. If the value of the test statistic on the original data set is more extreme than the test statistic on randomized data sets we can reject the null hypothesis. It is often not immediately clear why the null hypothesis is rejected. For example, the cost of clustering can be significantly lower in the original data than in the randomized data, but usually we would also like to know why the cost is small. We introduce a methodology for finding the smallest possible set of constraints, or patterns, that explains the data. In principle any type of patterns can be used as long as there exists an appropriate randomization method. We show that the problem is, in its general form, NP-hard, but that in a special case an exact solution can be computed fast, and propose a greedy algorithm that solves the problem. The proposed approach is demonstrated on time series data as well as on frequent itemsets in 0-1 matrices, and validated theoretically and experimentally

Implementation of an interactive pattern mining framework on electronic health record datasets

Large collections of electronic patient records contain a broad range of clinical information highly relevant for data analysis. However, they are maintained primarily for patient administration, and automated methods are required to extract valuable knowledge for predictive, preventive, personalized and participatory medicine. Sequential pattern mining is a fundamental task in data mining which can be used to find statistically relevant, non-trivial temporal dependencies of events such as disease comorbidities. This works objective is to use this mining technique to identify disease associations based on ICD-9-CM codes data of the entire Taiwanese population obtained from Taiwan’s National Health Insurance Research Database. This thesis reports the development and implementation of the Disease Pattern Miner – a pattern mining framework in a medical domain. The framework was designed as a Web application which can be used to run several state-of-the-art sequence mining algorithms on electronic health records, collect and filter the results to reduce the number of patterns to a meaningful size, and visualize the disease associations as an interactive model in a specific population group. This may be crucial to discover new disease associations and offer novel insights to explain disease pathogenesis. A structured evaluation of the data and models are required before medical data-scientist may use this application as a tool for further research to get a better understanding of disease comorbidities

A Spatial-based KDD Process to Better Understand the Spatiotemporal Phenomena

International audienceIn this paper, we present a knowledge discovery process ap- plied to hydrological data. To achieve this objective, we combine succes- sive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre processed in order to obtain different spatial proximities. Later, we apply two algorithms to extract spatiotemporal patterns and compare them. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data

MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both statistically-significant functions (i.e., patterns) when the available data is seen as a sample from an unknown distribution, and approximations of collections of high-expectation functions (e.g., frequent patterns) when the available data is a small sample from a large dataset. This feature is a strong improvement over previously proposed solutions that could only achieve one of the two. MCRapper uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself. To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining. TFP-R gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power (recall) than existing methods offering the same guarantees. We evaluate MCRapper and TFP-R and show that they outperform the state-of-the-art for their respective tasks