Association rule learning

Informatics, IT

doi:10.1049/pbtr038e_ch10

Applications of Machine Learning and Data Analytics Models in Maritime Transportation 2022

DOI: 10.1049/pbtr038e_ch10

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Association rule learning

IT Informatics¹

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Use of mining techniques to improve the effectiveness of marketing and sales

Malgaonkar

Surve

Hirave

2013

2013 International Conference on Advances in Technology and Engineering (ICATE)

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The mentioned system is designed to find the most frequent combinations of items. It is based on developing an efficient algorithm that outperforms the best available frequent pattern algorithms on a number of typical data sets. This will help in marketing and sales. The technique can be used to uncover interesting cross-sells and related products. Three different algorithms from association mining have been implanted and then best combination method is utilized to find more interesting results. The analyst then can perform the data mining and extraction and finally conclude the result and make appropriate decision.

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Use of mining techniques to improve the effectiveness of marketing and sales

Malgaonkar

Surve

Hirave

2013

2013 International Conference on Advances in Technology and Engineering (ICATE)

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A Dynamic Data-Driven Fine-Tuning Approach for Stacked Auto-Encoder Neural Network

Lin

Chiang²,

Hung³

et al. 2017

2017 IEEE 14th International Conference on E-Business Engineering (ICEBE)

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Discovering Knowledge Structure in the Web

Ramu

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Discovering Knowledge Structure in the Web by Siddharth Ramu In this project, we implement a new concept to extract knowledge or the semantic meaning from the Internet data. We apply the Association Rule and the apriori principle to a chosen set of documents from the Internet and analyze the associated benefits and drawbacks with this new concept. First, we find the highly relevant keywords in all documents by using the tf-idf. From these keywords, we find all keyword pairs (finite sequences of length 2), that are within a distance of 30 words. We then take keyword pairs with high frequency and find keyword triplets (finite sequence of length 3) … and so on, until there are no more high frequency finite keyword sequences. At each stage, we apply the Association Rule to find the primitive keywords that do not grow any longer. This is known as the Primitive Concept. We find the next set of keywords that can be associated from the non-primitive keywords. In this way, we find the longest keyword sequence that exists. We observe that this finite frequency of keywords often represents a concept in the web. For example, "closeness centrality" keyword pair represents social networking. Another generic example v could be "Wall Street". This keyword pair represents the New York stock exchange. In this project, we apply the Association Rule and the apriori principle on a set 21 IEEE papers on social networking. We limit our research to finding the primitive concept for each document and analyze the results obtained.

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Association rule learning

Cited by 3 publications

References 0 publications

Use of mining techniques to improve the effectiveness of marketing and sales

Use of mining techniques to improve the effectiveness of marketing and sales

A Dynamic Data-Driven Fine-Tuning Approach for Stacked Auto-Encoder Neural Network

Discovering Knowledge Structure in the Web

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