Big Data Analytics and Knowledge Discovery: 18th by Sanjay Madria, Takahiro Hara

By Sanjay Madria, Takahiro Hara

This ebook constitutes the refereed lawsuits of the 18th foreign convention on facts Warehousing and data Discovery, DaWaK 2016, held in Porto, Portugal, September 2016.

The 25 revised complete papers awarded have been rigorously reviewed and chosen from seventy three submissions. The papers are prepared in topical sections on Mining giant info, purposes of huge facts Mining, mammoth info Indexing and looking, sizeable facts studying and safeguard, Graph Databases and information Warehousing, facts Intelligence and Technology.

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Extra resources for Big Data Analytics and Knowledge Discovery: 18th International Conference, DaWaK 2016, Porto, Portugal, September 6-8, 2016, Proceedings (Lecture Notes in Computer Science)

Example text

A common request in the retail industry is finding a product’s associations with other products. This allows managers to obtain feedback on customer behavior and to propose relevant promotions. Instead of mining associations between c Springer International Publishing Switzerland 2016 S. Madria and T. ): DaWaK 2016, LNCS 9829, pp. 19–33, 2016. 1007/978-3-319-43946-4 2 20 M. Kirchgessner et al. popular products only, TopPI extracts itemsets for all items. By providing the analyst with an overview of the dataset, it facilitates the exploration of the results.

The parameter k controls the number of itemsets returned for each item, and may be tuned depending on the application. If the itemsets are directly presented to an analyst, k = 10 would be sufficient, while k = 500 may be used when those itemsets are post-processed. The paper is organized as follows. Section 2 defines the new semantics and our problem statement. The TopPI algorithm is fully described in Sect. 3. In Sect. 4, we present experimental results and compare TopPI against a simpler solution based on TFP [6].

Since then numerous methods using modularity either as an evaluation metric for validation or as an objective function for optimization have been proposed [2]. However, modularity based algorithms suffer from several drawbacks and does not guarantee the correct division of a network [1]. Some algorithms operate on graph diffusion based seed expansion strategy [4, 5]. These algorithms mostly use conductance as the optimization objective because conductance is considered the best scoring function when the network contains disjoint communities [4].

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