Mining high utility episodes in complex event sequences

Wu, Chengwei; Lin, Yu-Feng; Yu, Philip S.; Tseng, Vincent S.

doi:10.1145/2487575.2487654

Cited by 84 publications

(59 citation statements)

References 29 publications

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“…As stated above, all of these algorithms need an extra scan to the database, to determine the effective TWU of possible patterns discovered and select the truth high-utility patterns. High-utility pattern mining has also been expanded for mining more complex data types, such as sequences (e.g., USpan [77]) and episodes ( [74]), by extending the TWU model.…”

Section: Discussion and Open Issuesmentioning

confidence: 99%

Constrained pattern mining in the new era

Silva

Antunes

2015

Knowl Inf Syst

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Twenty years of research on frequent itemset mining, or pattern mining, has led to the existence of a set of efficient algorithms for identifying different types of patterns, from transactional to sequential. Despite the great advances in this field, big data brought a completely new context to operate, with new challenges arising from the growth in data size, dynamics and complexity. These challenges include the shift not only from static to dynamic data, but also from tabular to complex data sources, such as social networks (expressed as graphs) and data warehouses (expressed as multi-relational models). In this new context, and more than ever, users need effective ways to control the large number of discovered patterns, and to be able to choose what patterns to consider at each time. The most accepted and common approach to minimize these drawbacks has been to capture and represent the semantics of the domain through constraints, and use them not only to reduce the number of results, but also to focus the algorithms in areas where it is more likely to gain information and return more interesting results. The use of constraints in pattern mining has been widely studied, and there are a lot of proposed types of constraints and pushing strategies. In this paper, we present a new global view of the work done on the incorporation of constraints in the pattern mining process. In particular, we propose a new framework for constrained pattern mining, that allows us to organize and analyze existing algorithms and strategies, based on the different types and properties of constraints, and on the data sources they are able to handle.

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Section: Discussion and Open Issuesmentioning

confidence: 99%

Constrained pattern mining in the new era

Silva

Antunes

2015

Knowl Inf Syst

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“…One of the most notable approaches is that of episode mining [2,11,17,18,26], where one aims to find subsequences of events (episodes) that many sequences have in common. Because simply counting occurrences of subsequences may favor the most redundant ones, the task requires pruning techniques [2,18], measures of subsequence importance [13,26], or probabilistic modeling [11]. However, there are two drawbacks in these approaches [8,24].…”

Section: Related Workmentioning

confidence: 99%

“…Approaches based on mining frequently occurring subsequences [2,18,26] are not appropriate for this task, as the level of noise and large state-spaces mean that any specific pattern is extremely unlikely to appear repeatedly. Hidden Markov Models capture how latent states change [5,7,19,22], though they typically assume that all sequences share the same set of latent states and thus progress in the same way.…”

Section: Introductionmentioning

confidence: 99%

Finding progression stages in time-evolving event sequences

Yang

McAuley

Leskovec

et al. 2014

Proceedings of the 23rd International Conference on World Wide Web

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Event sequences, such as patients' medical histories or users' sequences of product reviews, trace how individuals progress over time. Identifying common patterns, or progression stages, in such event sequences is a challenging task because not every individual follows the same evolutionary pattern, stages may have very different lengths, and individuals may progress at different rates.In this paper, we develop a model-based method for discovering common progression stages in general event sequences. We develop a generative model in which each sequence belongs to a class, and sequences from a given class pass through a common set of stages, where each sequence evolves at its own rate. We then develop a scalable algorithm to infer classes of sequences, while also segmenting each sequence into a set of stages. We evaluate our method on event sequences, ranging from patients' medical histories to online news and navigational traces from the Web. The evaluation shows that our methodology can predict future events in a sequence, while also accurately inferring meaningful progression stages, and effectively grouping sequences based on common progression patterns. More generally, our methodology allows us to reason about how event sequences progress over time, by discovering patterns and categories of temporal evolution in large-scale datasets of events.

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“…Tseng et al presented an UPtree and designed UP-growth and UP-growth+with a set of effective strategies for pruning candidate itemsets for mining high-utility itemsets [27]. Several algorithms are still designed in progress to efficiently mine the high-utility itemsets from a static database [23,26,28,31].…”

Section: Mining High-utility Itemsetsmentioning

confidence: 99%

Updating high-utility pattern trees with transaction modification

Lin

Zhang

Gan

et al. 2014

Multimed Tools Appl

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Traditional association-rule mining only concerns the occurrence frequencies of the items in a binary database. In real-world applications, customers may buy several copies of the purchased items. Other factors such as profit, quantity, or price should be concerned to measure the utilities of the purchased items. High-utility itemsets mining was thus proposed to consider the factors of quantity and profit. Two-phase model was the most commonly way Multimed Tools Appl to keep the transaction-weighted utilization downward closure property, thus reducing the numerous candidates in utility mining. Most methods for finding high-utility itemsets are used to handle a static database. In practical applications, transactions are changed whether insertion, deletion, or modification. Some itemsets may arise as the new high-utility itemsets or become invalid knowledge in the updated database. In this paper, a maintenance Fast Updated High Utility Pattern tree for transaction MODification (FUP-HUP-tree-MOD) algorithm is thus proposed to effective maintain and update the built HUP tree for mining high-utility itemsets in dynamic databases without candidate generation. Experiments are conducted to show better performance of the proposed algorithm compared to the two-phase algorithm and the HUP tree algorithm in batch mode.

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Mining high utility episodes in complex event sequences

Cited by 84 publications

References 29 publications

Constrained pattern mining in the new era

Constrained pattern mining in the new era

Finding progression stages in time-evolving event sequences

Updating high-utility pattern trees with transaction modification

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