Information fusion from multiple databases using meta-association rules

Nowadays, data volume, distribution, and volatility make it difficult to search global patterns by applying traditional Data Mining techniques. In the case of data in a distributed environment, sometimes a local analysis of each dataset separately is adequate but some other times a global decision is needed by the analysis of the entire data. Association rules discovering methods typically require a single uniform dataset and managing with the entire set of distributed data is not possible due to its size. To address the scenarios in which satisfying this requirement is not practical or even feasible, we propose a new method for fusing information, in the form of rules, extracted from multiple datasets. The proposed model produces meta-association rules, i.e. rules in which the antecedent or the consequent may contain rules as well, for finding joint correlations among trends found individually in each dataset. In this paper, we describe the formulation and the implementation of two alternative frameworks that obtain, respectively, crisp meta-rules and fuzzy meta-rules. We compare our proposal with the information obtained when the datasets are not separated, in order to see the main differences between traditional association rules and meta-association rules. We also compare crisp and fuzzy methods for meta-association rule mining, observing that the fuzzy approach offers several advantages: it is more accurate since it incorporates the strength or validity of the previous information, produces a more manageable set of rules for human inspection, and allows the incorporation of contextual information to the mining process expressed in a more human-friendly format

Meta-association rules for mining interesting associations in multiple datasets

Association rules have been widely used in many application areas to extract new and useful information expressed in a comprehensive way for decision makers from raw data. However, raw data may not always be available, it can be distributed in multiple datasets and therefore there resulting number of association rules to be inspected is overwhelming. In the light of these observations, we propose meta-association rules, a new framework for mining association rules over previously discovered rules in multiple databases. Meta-association rules are a new tool that convey new information from the patterns extracted from multiple datasets and give a “summarized” representation about most frequent patterns. We propose and compare two different algorithms based respectively on crisp rules and fuzzy rules, concluding that fuzzy meta-association rules are suitable to incorporate to the meta-mining procedure the obtained quality assessment provided by the rules in the first step of the process, although it consumes more time than the crisp approach. In addition, fuzzy meta-rules give a more manageable set of rules for its posterior analysis and they allow the use of fuzzy items to express additional knowledge about the original databases. The proposed framework is illustrated with real-life data about crime incidents in the city of Chicago. Issues such as the difference with traditional approaches are discussed using synthetic data