Frequent pattern generation algorithms for Association Rule Mining : Strength and challenges

Soni, Hemant Kumar; Sharma, Sanjiv; Jain, Manisha

doi:10.1109/iceeot.2016.7755411

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…After determining the fuzzy clusters and membership functions, fuzzy association rules are generated on the fuzzified attributes with the FP-growth algorithm [40]. Association finds rules about items that appear together in an event such as a purchase transaction.…”

Section: Algorithm 1 Algorithm Of X-means Clusteringmentioning

confidence: 99%

Modeling and Querying Fuzzy SOLAP-Based Framework

Keskin

Yazıcı

2022

IJGI

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Nowadays, with the rise of sensor technology, the amount of spatial and temporal data is increasing day by day. Modeling data in a structured way and performing effective and efficient complex queries has become more essential than ever. Online analytical processing (OLAP), developed for this purpose, provides appropriate data structures and supports querying multidimensional numeric and alphanumeric data. However, uncertainty and fuzziness are inherent in the data in many complex database applications, especially in spatiotemporal database applications. Therefore, there is always a need to support flexible queries and analyses on uncertain and fuzzy data, due to the nature of the data in these complex spatiotemporal applications. FSOLAP is a new framework based on fuzzy logic technologies and spatial online analytical processing (SOLAP). In this study, we use crisp measures as input for this framework, apply fuzzy operations to obtain the membership functions and fuzzy classes, and then generate fuzzy association rules. Therefore, FSOLAP does not need to use predefined sets of fuzzy inputs. This paper presents the method used to model the FSOLAP and manage various types of complex and fuzzy spatiotemporal queries using the FSOLAP framework. In this context, we describe how to handle non-spatial and fuzzy spatial queries, as well as spatiotemporal fuzzy query types. Additionally, while FSOLAP primarily includes historical data and associated queries and analyses, we also describe how to handle predictive fuzzy spatiotemporal queries, which typically require an inference mechanism.

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Section: Algorithm 1 Algorithm Of X-means Clusteringmentioning

confidence: 99%

Modeling and Querying Fuzzy SOLAP-Based Framework

Keskin

Yazıcı

2022

IJGI

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“…These itemsets called frequent or large itemsets. The second subsection of the process identified association rule by large items sets which qualify minimum confidence [6][7].…”

Section: Basic Concepts and Terminologymentioning

confidence: 99%

“…These algorithms generated association mainly based on Apriori and Tree-based approach [2]. Apriori-based algorithms work in two phases, first to identify frequent patterns and second generate rules from these frequent patterns [7]. Association rule mining helpful in the various domain including e-commerce and time-series data analysis [19][20].…”

Section: Related Workmentioning

confidence: 99%

An Empirical Algorithm for High and Low Correlative Association Rule Mining

Soni¹,

Sharma²,

Upadhyay³

2018

IJIES

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Support and confidence based Association rules mining algorithms have certain problems. Although other metrics like interest factor, comprehensibility, lift, correlation etc. are available to measure the interestingness of association rules. All the objectives are not suitable for each and every situation. All the objectives which were proposed in the literature, have some drawback, like correlation analysis gives equal importance to the items those are present and absent in transaction database. Resultant the rules generated by this, sometime mislead decision makers. Hence there is a strong need to define some new objectives for association rules that support in effective decision making. In this paper, authors proposed two novel objectives, high correlation and low correlation for 2-variables and 3-variables. These novel objectives clearly indicate that how much or how less two/three items are correlated. On the basis of this, decision makers can form their business strategies. An empirical algorithm for high and low correlative association rules generation is also proposed. With numeric evolution and experiments on the real-life data set, effectiveness has been measured and found that proposed algorithm gives better results.

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“…An itemset ⊆ have a support s in D, denoted by s(X), if s% of transactions in D support X. There is a user-defined minimum support threshold, which is a fraction, i.e., a number in [0,1].…”

Section: Supportmentioning

confidence: 99%

Two Novel Pioneer Objectives of Association Rule Mining for high and low correlation of 2-varibales and 3-variables

Soni¹,

Sharma²,

A.K³

2017

IJET

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:Association rule generation is a significant research area of data mining, which find out the relation between the set of items . Significant association rule mainly based on two objectives -support and confidence. Some other metrics are also available to evaluate the goodness, effectiveness and interestingness of an association rule. Therefore, the association rule mining problem can be treated as multi-objective optimization problem. In this paper, we discuss the various objectives and their limitation. It is found that, each and every objective are not suitable in every situation. Other than this , most of the objectives are defined for 2-variables only. Simultaneously, in certain situation correlation analysis does not show the positive and negative correlation between items. Authors proposed two novel objectives, high correlation and low correlation for 2-variables and 3-variables. Through numerical analysis it is found that proposed objective clearly indicate about the positive and negative correlation among items. These objectives also gives appropriate solution in those cases, where previously defined objectives have some limitations. Simultaneously it also works in Simpson's paradox situation successfully.

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Frequent pattern generation algorithms for Association Rule Mining : Strength and challenges

Cited by 11 publications

References 6 publications

Modeling and Querying Fuzzy SOLAP-Based Framework

Modeling and Querying Fuzzy SOLAP-Based Framework

An Empirical Algorithm for High and Low Correlative Association Rule Mining

Two Novel Pioneer Objectives of Association Rule Mining for high and low correlation of 2-varibales and 3-variables

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