Mining Weighted Frequent Closed Episodes over Multiple Sequences

Frequent episode discovery is introduced to mine useful and interesting temporal patterns from sequential data. The existing episode mining methods mainly focused on mining from a single long sequence consisting of events with time constraints. However, there can be multiple sequences of different importance as the persons or entities associated with each sequence can be of different importance. Aiming to mine episodes in multiple sequences of different importance, we first define a new kind of episodes, i.e., the weighted frequent closed episodes, to take sequence importance, episode distribution and occurrence frequency into account together. Secondly, to facilitate the mining of such new episodes, we present a new concept called maximal duration serial episodes to cut a whole sequence into multiple maximum episodes using duration constraints, and discuss its properties for episode shrinking processing. Finally, based on the theoretical properties, we propose a two-phase approach to efficiently mine these new episodes. In Phase I, we adopt a level-wise episode shrinking framework to discover the candidate frequent closed episodes with the same prefixes, and in Phase II, we match the candidates with different prefixes to find the frequent close episodes. Experiments on simulated and real datasets demonstrate that the proposed episode mining strategy has good mining effectiveness and efficiency

Efficient Matching of Substrings in Uncertain Sequences

Substring matching is fundamental to data mining methods for se-quential data. It involves checking the existence of a short subse-quence within a longer sequence, ensuring no gaps within a match. Whilst a large amount of existing work has focused on substring matching and mining techniques for certain sequences, there are on-ly a few results for uncertain sequences. Uncertain sequences pro-vide powerful representations for modelling sequence behavioural characteristics in emerging domains, such as bioinformatics, sen-sor streams and trajectory analysis. In this paper, we focus on the core problem of computing substring matching probability in un-certain sequences and propose an efficient dynamic programming algorithm for this task. We demonstrate our approach is both com-petitive theoretically, as well as effective and scalable experimental-ly. Our results contribute towards a foundation for adapting classic sequence mining methods to deal with uncertain data.

Mining frequent serial episodes over uncertain sequence data

Data uncertainty has posed many unique challenges to nearly all types of data mining tasks, creating a need for uncertain data mining. In this paper, we focus on the particular task of mining probabilistic frequent serial episodes (P-FSEs) from uncertain sequence data, which applies to many real applications including sensor readings as well as customer purchase sequences. We first define the notion of P-FSEs, based on the frequentness probabilities of serial episodes under possible world semantics. To discover P-FSEs over an uncertain sequence, we propose: 1) an exact approach that computes the accurate frequentness probabilities of episodes; 2) an approximate approach that approximates the frequency of episodes using probability models; 3) an optimized approach that efficiently prunes a candidate episode by estimating an upper bound of its frequentness probability using approximation techniques. We conduct extensive experiments to evaluate the performance of the developed data mining algorithms. Our experimental results show that: 1) while existing research demonstrates that approximate approaches are orders of magnitudes faster than exact approaches, for P-FSE mining, the efficiency improvement of the approximate approach over the exact approach is marginal; 2) although it has been recognized that the normal distribution based approximation approach is fairly accurate when the data set is large enough, for P-FSE mining, the binomial distribution based approximation achieves higher accuracy when the the number of episode occurrences is limited; 3) the optimized approach clearly outperforms the other two approaches in terms of the runtime, and achieves very high accuracy. © 2013 ACM

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p