Mining uncertain data for frequent itemsets that satisfy aggregate constraints

Leung, Carson K.; Hao, Boyu; Brajczuk, Dale A.

doi:10.1145/1774088.1774305

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…One interesting example is the algorithm U-FPS, proposed by Leung and Brajczuk [39] to deal with constrained frequent pattern mining from uncertain data. It is able to represent user beliefs on the presence or absence of items in data, and also to push succinct [39] or (prefix-)monotone [40] constraints deep into the discovery process. Another example is the correspondence between pattern mining and constraint programming (with SAT solvers) [24,54].…”

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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“…As the mined frequent sets often serve as building blocks to other mining tasks (e.g., constrained association rule or pattern mining [14,17]), numerous frequent set mining algorithms have been proposed since the introduction of the classical Apriori algorithm [1]. An example is FP-growth [8], which builds a Frequent Pattern tree (FP-tree) to capture the information of static databases (DBs) of precise data so that frequent sets can be mined from the FP-tree.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Stream mining of frequent sets with limited memory

Cameron

Cuzzocrea²,

Leung

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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With advances in technology, streams of data are produced in many applications. Efficient techniques for extracting implicit, previously unknown, and potentially useful information (e.g., in the form frequent sets) from data streams are in demand. Many existing stream mining algorithms capture important streaming data and assume that the captured data can fit into main memory. However, problem arose when the available memory is so limited that such an assumption does not hold. In this paper, we propose a novel data structure called DSTable to capture important data from the streams onto the disk. The DSTable can be easily maintained; it can be applicable for mining frequent sets from datasets, especially in limited memory environments.

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“…Recently, several algorithms were designed to mine FIs from uncertain data [1,17,19]. For example, we proposed a tree-based algorithm called UF-growth [18] to mine static uncertain DBs for FIs.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Frequent itemset mining of uncertain data streams using the damped window model

Leung

Jiang

2011

Proceedings of the 2011 ACM Symposium on Applied Computing

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With advances in technology, large amounts of streaming data can be generated continuously by sensors in applications like environment surveillance. Due to the inherited limitation of sensors, these continuous data can be uncertain. This calls for stream mining of uncertain data. In recent years, tree-based algorithms have been proposed to use the sliding window model for mining frequent itemsets from streams of uncertain data. Besides the sliding window model, there are other window models for processing data streams. In this paper, we propose tree-based algorithms that use the damped window model to mine frequent itemsets from streams of uncertain data.

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Mining uncertain data for frequent itemsets that satisfy aggregate constraints

Cited by 12 publications

References 26 publications

Constrained pattern mining in the new era

Constrained pattern mining in the new era

Stream mining of frequent sets with limited memory

Frequent itemset mining of uncertain data streams using the damped window model

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