Research on Multi-Level Association Rules Based on Geosciences Data

Han, Demin; Shi, Yiyin; Wang, Wen; Dai, Yonghui

doi:10.4304/jsw.8.12.3269-3276

Cited by 8 publications

(10 citation statements)

References 16 publications

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“…In order to judge the quality of association rules, we introduced the scoring mechanism [5]: Score = 40%*support + 60%*confidence and the higher score the better quality on simulated faults. Table 1 presents the top 12 association rules used for the experiment on disposal of simulated faults.…”

Section: Results Validationmentioning

confidence: 99%

“…According to the basic idea of data fitting and a certain rule [5], spatial data of any irregular distribution are mathematically transformed so as to generate a set of atomic concepts and make the distributed spatial data become the superposition of several concept of different size, the basic idea is expressed in Eq. (1).…”

Section: Construction Of Association Rules Mining Model Based On Amelmentioning

confidence: 99%

“…Yu [4] attempts to evaluate the economics of wind power projects based on the cloud model. Han [5] imports the conceptual partition algorithm based on cloud model to finish the exchange from qualification to quantification. Expectation of some concept is not just a numeric value but in a sequence, so trapezoidal cloud makes description more accord with contiguous data.…”

Section: Introductionmentioning

confidence: 99%

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Research on Association Rules Reasoning and Application of Geosciences Data Based on Ameliorated Trapezoidal Cloud Transformation

2016

Socially Aware Organisations and Technologies. Impact and Challenges

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Part 4: Knowledge Management and EngineeringInternational audienceThis paper proposes an association rules reasoning model based on ameliorated trapezoidal cloud transformation. It is aimed primarily at complexity and randomness geosciences data bears. The traditional trapezoidal cloud transformation is improved in order to avoid lack of data mutation information and to finish reasonable and sensitive exchange from qualification to quantification. A set of attributes for simulating faults extraction algorithm is designed, which breaks through limitations of traditional visual interpretation and ensures an effectiveness and completeness of test data. Multi-Level Association Rules (MLAR) model [1] is also adopted to reason and predict unknown faults and fault properties in Chengdu Office zone. The result shows that the MLAR algorithm enhanced an association mining between fault types with their classified attributes

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Section: Results Validationmentioning

confidence: 99%

Section: Construction Of Association Rules Mining Model Based On Amelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Association Rules Reasoning and Application of Geosciences Data Based on Ameliorated Trapezoidal Cloud Transformation

2016

Socially Aware Organisations and Technologies. Impact and Challenges

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“…It is especially for the case of multilevel association rule mining approaches to discover interesting relations among data elements with multiple levels of abstractions. Successful applications include spatial data analysis [3], emergency event analysis [4], sensor network data mining [5], and gene ontology mining. However, most existing multilevel association rules mining algorithms rely on exhaustive scans of the database to find frequent patterns across different abstraction levels, such as the most renowned Apriori algorithm [6] and Frequent Pattern tree algorithm (FP-tree) [7].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

Zeng

Liu

2014

Mathematical Problems in Engineering

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Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP-growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic-based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold.

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“…Data mining with its unlimited diversity of techniques and approaches may be applicable to retrieve knowledge at any kind of information repositories [1] like sensor network data mining [2], gene ontology mining [3], cloud computing [4], spatial data mining [5,6], network intrusion detection [7,8] and many more. The information retrieval from the transactional database is becoming very tedious because it may include large number of concept hierarchies.…”

Section: Introductionmentioning

confidence: 99%

Representation of Concept Hierarchy using an Efficient Encoding Scheme

Yadav¹,

Garg²,

Vishav³

2015

IJCA

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The premise of this paper is to use an efficient encoding scheme which will be used to encode high level concept hierarchy of a transactional table. This table will work as the base to generate multiple level association rules. These rules discovers the hidden knowledge align at higher level of abstraction. Therefore the numeric encoding of the concept hierarchy improves the time complexity and space complexity of task relevant data.

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Research on Multi-Level Association Rules Based on Geosciences Data

Cited by 8 publications

References 16 publications

Research on Association Rules Reasoning and Application of Geosciences Data Based on Ameliorated Trapezoidal Cloud Transformation

Research on Association Rules Reasoning and Application of Geosciences Data Based on Ameliorated Trapezoidal Cloud Transformation

A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

Representation of Concept Hierarchy using an Efficient Encoding Scheme

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