Mining frequent closed patterns in pointset databases

Lee, Anthony J. T.; Tsao, Wen-Kwang; Chen, Po-Yin; Lin, Ming-Chih; Yang, Shih-Hui

doi:10.1016/j.is.2009.10.007

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…For example, F(C support, C weight ) is a constraint function to satisfy both weight and support constraints. In closed frequent pattern mining, 4,16,17,31,36 a small set of representative patterns called closed frequent patterns are mined. Mining closed frequent patterns is semantically the same as mining a complete set of frequent patterns.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints

Yun

Yoon

2014

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Based on the frequent pattern mining, closed frequent pattern mining and weighted frequent pattern mining have been studied to reduce the search space and discover important patterns. In the previous definition of weighted closed patterns, supports of patterns are only considered to compute the closures of the patterns. It means that the closures of weighted frequent patterns cannot be perfectly checked. Moreover, the usefulness of weighted closed frequent patterns depends on the presence of frequent patterns that have supersets with the exactly same weighted support. However, from the errors such as noise, slight changes in items' supports or weights by them have significantly negative effects on the mining results, which may prevent us from obtaining exact and valid analysis results since the errors can break the original characteristics of items and patterns. In this paper, to solve the above problems, we propose a concept of robust weighted closed frequent pattern mining, and an approximate bound is defined on the basis of the concept, which can relax requirements for precise equality among patterns' weighted supports. Thereafter, we propose a weighted approximate closed frequent pattern mining algorithm which not only considers the two approaches but also suggests fault tolerant pattern mining in the noise constraints. To efficiently mine weighted approximate closed frequent patterns, we suggest pruning and subset checking methods which reduce search space. We also report extensive performance study to demonstrate the effectiveness, efficiency, memory usage, scalability, and quality of patterns in our algorithm.

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Section: Related Workmentioning

confidence: 99%

An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints

Yun

Yoon

2014

Int. J. Unc. Fuzz. Knowl. Based Syst.

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“…Healthcare fraud literature originating from countries outside of the United States has used a variety of sources to acquire medical claims data. The National Health Insurance Administration (NHIA) in Taiwan has provided data to multiple research groups (Chan & Lan, 2001;Hwang, Wei & Yang, 2003;Liou, Tang & Chen, 2008;Wei, Hwang & Yang, 2000;Yang & Hwang, 2006), and an analogous NHIA program in South Korea, the National Health Insurance (NHI) system, has also contributed to studies (Shin, Park, Lee & Jhee, 2012). Two major Australian governmental health departments, the Health Insurance Commission (HIC) and Medicare Australia, have been reported as the data sources in numerous pertinent research projects (He, Graco & Yao, 1999;Hubick 1992;Shan, Jeacocke, Murray & Sutinen, 2008;Shan, Murray & Sutinen, 2009;Tang, Mendis, Murray et al, 2011;Williams, 1999).…”

Section: Introductionmentioning

confidence: 99%

Statistical Modeling for Studying the Impact of ICD-10 on Health Fraud Detection

Olson

2017

International Journal of Privacy and Health Information Management

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When an individual is seen or treated by a healthcare professional, a series of alphanumeric codes are used to describe the medical diagnoses and services provided. This designated classification structure, the ninth iteration of ICD (International Classification of Diseases), implements the use of coding for healthcare management, public health and medical informatics, and insurance purposes. ICD-9 has been the coding standard in the healthcare industry for 30 years. On October 1st, 2015, the tenth revision ICD-10 was formally implemented in the United States. This paper explores the validity of predictions from domain professionals regarding fraud detection and the implementation of the ICD-10 code set. The notion that fraud detection systems using supervised learning algorithms will encounter an initial decline in performance due to ICD-10 is fairly unsupported at the moment. The authors claim that the results from their study will provide evidence that will support this notion of a preliminary negative transitional impact.

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“…Another alternative approach is Reduced Apriori Algorithm with Tag, which improves upon the efficiency of the pruning operation and reduces the burden of candidate generation [20]. Several other algorithms were proposed that focus on closed itemsets, and are able to prune and reduce the data a faster rate, yielding quicker runtimes [22][23][24]. A variety of flavors and combinations of tuning mechanisms provide incremental improvements to the underlying logic workflow of candidate generation and support filtration.…”

Section: Apriorimentioning

confidence: 99%

Distributed frequent hierarchical pattern mining for robust and efficient large-scale association discovery

Phinney¹

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Frequent pattern mining is a classic data mining technique, generally applicable to a wide range of application domains, and a mature area of research. The fundamental challenge arises from the combinatorial nature of frequent itemsets, scaling exponentially with respect to the number of unique items. Apriori-based and FPTree-based algorithms have dominated the space thus far. Initial phases of this research relied on the Apriori algorithm and utilized a distributed computing environment; we proposed the Cartesian Scheduler to manage Apriori's candidate generation process. To address the limitation of bottom-up frequent pattern mining algorithms such as Apriori and FPGrowth, we propose the Frequent Hierarchical Pattern Tree (FHPTree): a tree structure and new frequent pattern mining paradigm. The classic problem is redefined as frequent hierarchical pattern mining where the goal is to detect frequent maximal pattern covers. Under the proposed paradigm, compressed representations of maximal patterns are mined using a top-down FHPTree traversal, FHPGrowth, which detects large patterns before their subsets, thus yielding significant reductions in computation time. The FHPTree memory footprint is small; the number of nodes in the structure scales linearly with respect to the number of unique items. Additionally, the FHPTree serves as a persistent, dynamic data structure to index frequent patterns and enable efficient searches. When the search space is exponential, efficient targeted mining capabilities are paramount; this is one of the key contributions of the FHPTree. This dissertation will demonstrate the performance of FHPGrowth, achieving a 300x speed up over state-of-the-art maximal pattern mining algorithms and approximately a 2400x speedup when utilizing FHPGrowth in a distributed computing environment. In addition, we allude to future research opportunities, and suggest various modifications to further optimize the FHPTree and FHPGrowth. Moreover, the methods we offer will have an impact on other data mining research areas including contrast set mining as well as spatial and temporal mining.

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Mining frequent closed patterns in pointset databases

Cited by 8 publications

References 26 publications

An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints

An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints

Statistical Modeling for Studying the Impact of ICD-10 on Health Fraud Detection

Distributed frequent hierarchical pattern mining for robust and efficient large-scale association discovery

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