Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closure Property

Bras, Yannick Le; Lenca, Philippe; Lallich, Stéphane

doi:10.1007/978-1-4419-1280-0_4

Cited by 11 publications

(3 citation statements)

References 46 publications

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“…Le Bras et al propose the general universal existential upward closure (GUEUC) in [23] and show that 13 quality measures out of the 32 they study verify this property. Authors also show how these measures can be integrated in a generate-and-test mining algorithm.…”

Section: Top-quality Associations Miningmentioning

confidence: 94%

TopPI: An efficient algorithm for item-centric mining

Leroy

Kirchgessner

Termier

et al. 2017

Information Systems

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International audienceIn this paper, we introduce item-centric mining, a new semantics for mining long-tailed datasets. Our algorithm, TopPI, finds for each item its top-k most frequent closed itemsets. While most mining algorithms focus on the globally most frequent itemsets, TopPI guarantees that each item is represented in the results, regardless of its frequency in the database. TopPI allows users to efficiently explore Web data, answering questions such as " what are the k most common sets of songs downloaded together with the ones of my favorite artist? ". When processing retail data consisting of 55 million supermarket receipts, TopPI finds the itemset " milk, puff pastry " that appears 10,315 times, but also " frangipane, puff pastry " and " nori seaweed, wasabi, sushi rice " that occur only 1120 and 163 times, respectively. Our experiments with analysts from the marketing department of our retail partner, demonstrate that item-centric mining discover valuable itemsets. We also show that TopPI can serve as a building-block to approximate complex itemset ranking measures such as the p-value. Thanks to efficient enumeration and pruning strategies, TopPI avoids the search space explosion induced by mining low support itemsets. We show how TopPI can be parallelized on multi-cores and distributed on Hadoop clusters. Our experiments on datasets with different characteristics show the superiority of TopPI when compared to standard top-k solutions, and to Parallel FP-Growth, its closest competitor

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Section: Top-quality Associations Miningmentioning

confidence: 94%

TopPI: An efficient algorithm for item-centric mining

Leroy

Kirchgessner

Termier

et al. 2017

Information Systems

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“…But it implies the search of an adequate (usually low) frequency threshold, as in LAMP [10], which is not affordable at our scale. Le Bras et al show how 13 other quality measures can be integrated in a generateand-test mining algorithm [8]. But, as with Apriori, the candidate generation step is unfeasible at our scale.…”

Section: Example Itemsetsmentioning

confidence: 99%

TopPI: An Efficient Algorithm for Item-Centric Mining

Kirchgessner

Leroy

Termier

et al. 2016

Big Data Analytics and Knowledge Discovery

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Abstract. We introduce TopPI, a new semantics and algorithm designed to mine long-tailed datasets. For each item, and regardless of its frequency, TopPI finds the k most frequent closed itemsets that item belongs to. For example, in our retail dataset, TopPI finds the itemset "nori seaweed, wasabi, sushi rice, soy sauce" that occurrs in only 133 store receipts out of 290 million. It also finds the itemset "milk, puff pastry", that appears 152,991 times. Thanks to a dynamic threshold adjustment and an adequate pruning strategy, TopPI efficiently traverses the relevant parts of the search space and can be parallelized on multi-cores. Our experiments on datasets with different characteristics show the high performance of TopPI and its superiority when compared to state-of-the-art mining algorithms. We show experimentally on real datasets that TopPI allows the analyst to explore and discover valuable itemsets.

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“…The interpretability of DM procedures, and classification models in particular, is also taken up by Le Bras et al [31]. They focus on rule-based classifiers, which are commonly credited for being (relatively easily) comprehensible.…”

Section: Knowledge Discovery From Supervised Learningmentioning

confidence: 99%

Data Mining and Information Systems: Quo Vadis?

Stahlbock

Lessmann

Crone

2009

Annals of Information Systems

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Information and communication technology has been a steady source of innovations which have considerably impacted the way companies conduct business in the digital as well as the physical world. Today, information systems (IS) holistically support virtually all aspects of corporations and nonprofit institutions, along internal processes from purchasing and operations management toward sales, marketing, and eventually the customer (horizontally along the supply chain), from these operational functions toward finance, accounting, and upper management activities (vertically across the hierarchy) and externally to collaborate with external partners, suppliers, or customers. The holistic support of internal business processes and external relationships by means of IS has, in turn, led to the vast growth of internal and external data being stored and processed within corporate environments.The progressive gathering of very large and heterogeneous data sets, accompanied by the increasing computational power and evolving database technology, summoned an increasing interest in data mining (DM) as a (novel) tool for discovering knowledge in data. In addition to technological advances, the success of DM -at least in corporate environments -can also be attributed to changes in the business environment. For example, increasing competition through the advent of electronic commerce and the removal barriers for new market entrants, more informed and thus

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Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closure Property

Cited by 11 publications

References 46 publications

TopPI: An efficient algorithm for item-centric mining

TopPI: An efficient algorithm for item-centric mining

TopPI: An Efficient Algorithm for Item-Centric Mining

Data Mining and Information Systems: Quo Vadis?

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