Incorporating SAT solvers into hierarchical clustering algorithms

Gilpin, Sean; Davidson, Ian

doi:10.1145/2020408.2020585

Cited by 20 publications

(10 citation statements)

References 15 publications

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“…Another interesting direction is the integration of SATs as an efficient and effective approach for solving the constrained clustering problems, e.g. [30]. In particular, we aim at densitybased clustering [20], a fundamental data clustering techniques with many extensions and applications [43,44,46,6,45,16,42,40,41].…”

Section: Discussionmentioning

confidence: 99%

Solving the all-interval series problem

Nguyen

Son

2014

Proceedings of the Fifth Symposium on Information and Communication Technology - SoICT '14

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Although Boolean satisfiability (abbreviated as SAT) is a sub-field of constraint programming (CP), the former states and solves problems as a black-box approach, whereas the latter aims at being tunable and programmable. Although many researches bridging SAT and CP have been provided, surprisingly, only few researchers have compared the SAT and CP approaches on a particular problem. This paper studies how to solve the all-interval series problem through both approaches. We will show that by using a state-of-theart SAT solver and an appropriate SAT encoding the SAT approach obtains a significantly higher performance over the CP approach. Furthermore, we also provide the state-of-theart result for several all-interval series instances.

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Section: Discussionmentioning

confidence: 99%

Solving the all-interval series problem

Nguyen

Son

2014

Proceedings of the Fifth Symposium on Information and Communication Technology - SoICT '14

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“…A framework formalizing hierarchical clustering as an Integer Linear Programming problem has recently been proposed by Gilpin et al [37]. Gilpin et al also propose in [38] a framework based on SAT for hierarchical constrained clustering with different types of user constraints.…”

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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“…Hierarchical clustering is used extensively to organize documents [18,17,5], images [2] and even graphs [13] into a hierarchy. The hierarchical organization of these objects has Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Efficient hierarchical clustering of large high dimensional datasets

Gilpin

Qian²,

Davidson

2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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Hierarchical clustering is extensively used to organize high dimensional objects such as documents and images into a structure which can then be used in a multitude of ways. However, existing algorithms are limited in their application since the time complexity of agglomerative style algorithms can be as much as O(n 2 log n) where n is the number of objects. Furthermore the computation of similarity between such objects is itself time consuming given they are high dimension and even optimized built in functions found in MATLAB take the best part of a day to handle collections of just 10,000 objects on typical machines. In this paper we explore using angular hashing to hash objects with similar angular distance to the same hash bucket. This allows us to create hierarchies of objects within each hash bucket and to hierarchically cluster the hash buckets themselves. With our formal guarantees on the similarity of objects in the same bucket this leads to an elegant agglomerative algorithm with strong performance bounds. Our experimental results show that not only is our approach thousands of times faster than regular agglomerative algorithms but surprisingly the accuracy of our results is typically as good and can sometimes be substantially better.

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Incorporating SAT solvers into hierarchical clustering algorithms

Cited by 20 publications

References 15 publications

Solving the all-interval series problem

Solving the all-interval series problem

Constrained clustering by constraint programming

Efficient hierarchical clustering of large high dimensional datasets

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