The Top-k Frequent Closed Itemset Mining Using Top-k SAT Problem

Jabbour, Saïd; Saïs, Lakhdar; Salhi, Yakoub

doi:10.1007/978-3-642-40994-3_26

Cited by 28 publications

(33 citation statements)

References 19 publications

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“…Constraint Programming has already been shown to be a promising approach for Data Mining through various tasks, such as itemset mining [40][41][42][43][44], skypattern mining [45] or decision tree construction [46].…”

Section: First Model Second Modelmentioning

confidence: 99%

Constrained clustering by constraint programming

Dao

Duong

Vrain

2017

Artificial Intelligence

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Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often exact methods, which either enumerate all the solutions satisfying the user constraints, or find a global optimum when an optimization criterion is specified. In a previous work, we have proposed a model for Constrained Clustering based on a Constraint Programming framework. It is declarative, allowing a user to integrate user constraints and to choose an optimization criterion among several ones. In this article we present a new and substantially improved model for Constrained Clustering, still based on a Constraint Programming framework. It differs from our earlier model in the way partitions are represented by means of variables and constraints. It is also more flexible since the number of clusters does not need to be set beforehand; only a lower and an upper bound on the number of clusters have to be provided. In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms. We show that such a framework can easily be embedded in a more general process and we illustrate this on the problem of finding the optimal Pareto front of a bi-criterion constrained clustering task. We compare our approach with existing exact approaches, based either on a branch-and-bound approach or on graph coloring on twelve datasets. Experiments show that the model outperforms exact approaches in most cases.

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Section: First Model Second Modelmentioning

confidence: 99%

Constrained clustering by constraint programming

Dao

Duong

Vrain

2017

Artificial Intelligence

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“…However, these methods require to fix the number of local patterns included in a pattern set, a strong limitation in practice, and tend to have scaling problems. Recent contributions also employ more specialized systems such as satisfiability solvers [9] and integer linear programming techniques [1,16]. These methods address particular problem settings whereas we propose a declarative and exhaustive method based on ILP returning the best solution according to an optimization criterion, and which is able to handle a wide variety of constraints and therefore various different pattern set mining tasks.…”

Section: Related Workmentioning

confidence: 99%

Integer Linear Programming for Pattern Set Mining; with an Application to Tiling

Ouali¹,

Zimmermann²,

Loudni³

et al. 2017

Advances in Knowledge Discovery and Data Mining

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Pattern set mining is an important part of a number of data mining tasks such as classification, clustering, database tiling, or pattern summarization. Efficiently mining pattern sets is a highly challenging task and most approaches use heuristic strategies. In this paper, we formulate the pattern set mining problem as an optimization task, ensuring that the produced solution is the best one from the entire search space. We propose a method based on integer linear programming (ILP) that is exhaustive, declarative and optimal. ILP solvers can exploit different constraint types to restrict the search space, and can use any pattern set measure (or combination thereof) as an objective function, allowing the user to focus on the optimal result. We illustrate and show the efficiency of our method by applying it to the tiling problem.

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“…In reference [24], [25], [26] the Top-k queries have been introduced in a quantitative preference model setting, that is, where preference between tuples is expressed by a score function defined over the dataset. The Top-k dominating queries have been introduced in [27] as an extension of the skyline queries of [17] which were originally designed to return the most preferred tuples, without any user control on the size of the result.…”

Section: Related Workmentioning

confidence: 99%

A New Top-k Conditional XML Preference Queries

Alhazmi¹,

Ykhlef²

2014

IJAIA

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Preference querying technology is a very important issue in a variety of applications ranging from ecommerce to personalized search engines. Most of recent research works have been dedicated to this topic in the Artificial Intelligence and Database fields. Several formalisms allowing preference reasoning and specification have been proposed in the Artificial Intelligence domain. On the other hand, in the Database field the interest has been focused mainly in extending standard Structured Query Language (SQL) and also eXtensible Markup Language (XML) with preference facilities in order to provide personalized query answering. More precisely, the interest in the database context focuses on the notion of Top-k preference query and on the development of efficient methods for evaluating these queries. A Top-k preference query returns k data tuples which are the most preferred according to the user's preferences. Of course, Top-k preference query answering is closely dependent on the particular preference model underlying the semantics of the operators responsible for selecting the best tuples. In this paper, we consider the Conditional Preference queries (CP-queries) where preferences are specified by a set of rules expressed in a logical formalism. We introduce Top-k conditional preference queries (Top-k CP-queries), and the operators BestK-Match and Best-Match for evaluating these queries will be presented.

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The Top-k Frequent Closed Itemset Mining Using Top-k SAT Problem

Cited by 28 publications

References 19 publications

Constrained clustering by constraint programming

Constrained clustering by constraint programming

Integer Linear Programming for Pattern Set Mining; with an Application to Tiling

A New Top-k Conditional XML Preference Queries

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