We introduce an algorithm for mining expressive temporal relationships from complex data. Our algorithm, AprioriSetsAndSequences (ASAS), extends the Apriori algorithm to data sets in which a single data instance may consist of a combination of attribute values that are nominal sequences, time series, sets, and traditional relational values. Datasets of this type occur naturally in many domains including health care, financial analysis, complex system diagnostics, and domains in which multi-sensors are used. AprioriSetsAndSequences identifies predefined events of interest in the sequential data attributes. It then mines for association rules that make explicit all frequent temporal relationships among the occurrences of those events and relationships of those events and other data attributes. Our algorithm inherently handles different levels of time granularity in the same data set. We have implemented AprioriSetsAndSequences within the Weka environment and have applied it to computer performance, stock market, and clinical sleep disorder data. We show that AprioriSetsAndSequences produces rules that express significant temporal relationships that describe patterns of behavior observed in the data set.

Data mining techniques have been widely applied to extract desirable knowledge from existing databases for specific purposes. In real-world applications, a database usually involves the time periods when transactions occurred and exhibition periods of items, in addition to the items bought in the transactions. To handle this kind of data, temporal data mining techniques are thus proposed to find temporal association rules from a database with time. Most of the existing studies only consider different item lifespans to find general temporal association rules, and this may neglect some useful information. For example, while an item within the whole exhibition period may not be a frequent one, it may be frequent within part of this time. To deal with this, the concept of a hierarchy of time is thus applied to temporal data mining along with suitable time granules, as defined by users. In this thesis, we thus handle the problem of mining temporal association rules with a hierarchy of time granules from a temporal database, and also propose three novel mining algorithms for different item lifespan definitions. In the first definition, the lifespan of an item in a time granule is calculated from the first appearance time to the end time in the time granule. In the second definition, the lifespan of an item in a time granule is evaluated from the publication time of the item to the end time in the time granule. Finally, in the third definition, the lifespan of an item in a time granule is measured by its entire exhibition period. The experimental results on a simulation dataset show the performance of the three proposed algorithms under different item lifespan definitions, and compare the mined temporal association rules with and without consideration of the hierarchy of time granules under different parameter settings.

There are two main contributions in the thesis . Firstly, we design a novel and efficient algorithm for mining calendar-based association rules which have multilevel time granularities in temporal databases. Unlike apriori-like approaches , our method scans the database twice at most. By avoiding multiple scans over the database , our method can reduce the database scanning time. Secondly, we use membership functions to construct fuzzy calendar patterns which represent asynchronous periods. With the use of fuzzy calendar patterns, we can discover fuzzy periodical association rules which are association rules occurring in asynchronous periods. Experimental results have shown that our method is more efficient than others, and we can find fuzzy periodical association rules satisfactorily.

This thesis focuses on mining association rules on multivariate time series. Com-mon association rule mining algorithms can usually only be applied to transactional data, and a typical application is market basket analysis. If we want to mine temporal association rules on time series data, changes need to be made. During temporal association rule mining, the temporal ordering nature of data and the temporal interval between the left and right patterns of a rule need to be considered. This thesis reviews some mining methods for temporal association rule mining, and proposes two similar algorithms for the mining of frequent patterns in single and multivariate time series. Both algorithms are scalable and efficient. In addition, temporal association rules are generated from the patterns found. Finally, the usability and efficiency of the algorithms are demonstrated by evaluating the results.

No description available.