An efficient mining algorithm for maximal weighted frequent patterns in transactional databases

Yun, Unil; Shin, Hyeonil; Ryu, Keun Ho; Yoon, Eunchul

doi:10.1016/j.knosys.2012.02.002

Cited by 35 publications

(15 citation statements)

References 41 publications

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“…Table 2 presents the statistical information on data sets, which are used in the examination. The strength of the entire dissimilar items is shown in the last column of Table 2; they are mostly found in [13,16] indicate that Apriori works well for such type of data sets, when the item numbers are reduced, but it does not work well when the patterns are extensive. FPs may be large and/or insignificant maintenance thresholds.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 98%

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Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model

Javaid

Memon

Talpur

et al. 2016

Mehran Univ. res. j. eng. technol.

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EPs (Extracting Frequent Patterns) from the continuous transactional data streams is a challenging and critical task in some of the applications, such as web mining, data analysis and retail market, prediction and network monitoring, or analysis of stock market exchange data. Many algorithms have been developed previously for mining FPs (Frequent Patterns) from a data stream. Such algorithms are currently highly required to develop new solutions and approaches to the precise handling of data streams. New techniques, solutions, or approaches are developed to address unbounded, ordered, and continuous sequences of data and for the generation of data at a rapid speed from data streams. Hence, extracting FPs using fresh or recent data involves the high-level analysis of data streams. We have suggested an efficient technique for the window sliding model; this technique extracts new and fresh FPs from high-speed data streams. In this study, a CPILT (Compacted Tree Compact Pattern Tree) is developed to capture the latest contents in the stream and to efficiently remove outdated contents from the data stream. The main concept introduced in this work on CPILT is the dynamic restructuring of a tree, which is helpful in producing a compacted tree and the frequency descending structure of a tree on runtime. With the help of the mining technique of FP growth, a complete list of new and fresh FPs is obtained from a CPILT using an existing window. The memory usage and time complexity of the latest FPs in high-speed data streams can efficiently be determined through proper experimentation and analysis. Therefore, the tracks of the last visited batches are maintained via FPM, and an extra pointer is used for every node to count the last restructured batch number.Frequency list contents are changed for SW reflection using nodes. The FP-growth technique is utilized for mining when the information is captured using SW [7], that is, the FPs from a complete information sets.However, FPM has several limitations. First, information or item sets are stored in the canonical order, and using Hence, the updating process for a frequency list related to specified nodes is not performed, given new incoming batches that are not visited. For the current window, FPM may leave some invalid nodes during updating. Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model MehranHence, for mining, FPM consumes more time than such types of trees based on structures organized in such a way that the frequency depends on the order of items.Moreover, FPM construction is totally based on an assumption that does not consider the limitations of the main memory, which is unrealistic in considering or processing huge amounts of data, such as a data stream.By contrast, CPILT, the proposed tree in this study, exactly provides similar information on data streams and performs similarly to FPM, such action with the storage only in an FP tree with a strong compact structure, thereby presenting an efficient...

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Section: Experimental Analysis and Resultsmentioning

confidence: 98%

“…[12] have suggested the "lossy weight algorithm", which is mostly used in finding frequent item sets based on weight. The "SW" approach has also been proposed [4,13] and is mostly used in data stream mining. This approach is subdivided in to two types: the "transaction-sensitive" SW and the "time-sensitive" SW.…”

Section: Related Workmentioning

confidence: 99%

Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model

Javaid

Memon

Talpur

et al. 2016

Mehran Univ. res. j. eng. technol.

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show abstract

“…can be mined to discover hidden patterns (Alavi and Hashemi, 2015;Duong et al, 2014;Han et al, 2004). For acquiring further interesting and realistic patterns, weighted frequent pattern (Ahmed et al, 2012;Yun et al, 2012), weighted periodic pattern (Yang et al, 2014), high utility pattern (Ahmed et al, 2009) and data stream (Yun et al, 2014) mining methods were developed, but most of them are limited to transactional databases or streaming databases. Directed Acyclic Graphs (DAGs) are useful data structure to represent special kind of data, like procedural abstraction, code compaction, event sequences, etc.…”

Section: Related Workmentioning

confidence: 99%

A new framework for mining frequent interaction patterns from meeting databases

Fariha

Ahmed

Leung

et al. 2015

Engineering Applications of Artificial Intelligence

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“…As another approach for the closure property, closed frequent sequential pattern mining 11 has also been suggested. Meanwhile, in weighted frequent pattern mining, 3,30,33,37,39,41 important patterns with priority are first found, where a weight of the pattern is an average value of weights of items within the pattern, and a weighted support of a pattern is the resultant value of multiplying the pattern's support by the weight of the pattern. In other words, the weight and weighted support of a pattern are defined as follows.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, there are other applications for the weight constraints such as mining weighted frequent patterns in single-pass incremental and interactive environment, 3 mining weighted maximal frequent patterns. 41 Furthermore, there are researches for compressing frequent patterns and approximating a set of frequent k patterns and obtaining a compact frequent pattern base. 34 Recently, Refs.…”

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.

Self Cite

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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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An efficient mining algorithm for maximal weighted frequent patterns in transactional databases

Cited by 35 publications

References 41 publications

Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model

Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model

A new framework for mining frequent interaction patterns from meeting databases

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

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