Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining

• URL: http://arxiv.org/abs/2211.12767v1
• Date: Wed, 23 Nov 2022 08:34:05 GMT
• Title: Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining
• Authors: Th\'eophile Berteloot, Richard Khoury, Audrey Durand
• Abstract summary: Association rule mining is one of the most studied research fields of data mining. We compare the performances of state-of-the-art meta-heuristics on the association rule mining problem. We propose a new algorithm designed to mine rules efficiently from massive datasets by exploring a large variety of solutions.
• Score: 5.175050215292647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to highly explainable classification systems. Classical association rule mining algorithms have several flaws especially with regards to their execution times, memory usage and number of rules produced. An alternative is the use of meta-heuristics, which have been used on several optimisation problems. This paper has two objectives. First, we provide a comparison of the performances of state-of-the-art meta-heuristics on the association rule mining problem. We use the multi-objective versions of those algorithms using support, confidence and cosine. Second, we propose a new algorithm designed to mine rules efficiently from massive datasets by exploring a large variety of solutions, akin to the explosion of species diversity of the Cambrian Explosion. We compare our algorithm to 20 benchmark algorithms on 22 real-world data-sets, and show that our algorithm present good results and outperform several state-of-the-art algorithms.

Related papers

• Learning Arithmetic Formulas in the Presence of Noise: A General Framework and Applications to Unsupervised Learning [4.10375234787249]
  We present a framework for designing efficient algorithms for unsupervised learning problems. Our framework is based on a meta algorithm that learns arithmetic circuits in the presence of noise.
  arXiv  Detail & Related papers  (2023-11-13T12:26:25Z)
• Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit [65.268245109828]
  We first introduce the Combinatorial Successive Asign algorithm, which is the first algorithm that can identify the best action even when the size of the action class is exponentially large. We show that the upper bound of the probability of error of the CSA algorithm matches a lower bound up to a logarithmic factor in the exponent. We experimentally compare the algorithms with previous methods and show that our algorithm performs better.
  arXiv  Detail & Related papers  (2023-10-24T09:47:32Z)
• ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms [5.478671305092084]
  We introduce ParlayANN, a library of deterministic and parallel graph-based approximate nearest neighbor search algorithms. We develop novel parallel implementations for four state-of-the-art graph-based ANNS algorithms that scale to billion-scale datasets.
  arXiv  Detail & Related papers  (2023-05-07T19:28:23Z)
• Association Rules Mining with Auto-Encoders [5.175050215292647]
  We present an auto-encoder solution to mine association rule called ARM-AE. Our algorithm discovers high support and confidence rule set and has a better execution time than classical methods.
  arXiv  Detail & Related papers  (2023-04-26T17:55:44Z)
• Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees [79.59010418610625]
  We study differentially private approximation algorithms for hierarchical clustering. We show strong lower bounds for the problem: that any $epsilon$-DP algorithm must exhibit $O(|V|2/ epsilon)$-additive error for an input dataset. We propose a private $1+o(1)$ approximation algorithm which also recovers the blocks exactly.
  arXiv  Detail & Related papers  (2023-01-31T19:14:30Z)
• HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection [75.84584400866254]
  We propose a new algorithm selector leveraging special forests, combining the strengths of both approaches while alleviating their weaknesses. HARRIS' decisions are based on a forest model, whose trees are created based on optimized on a hybrid ranking and regression loss function.
  arXiv  Detail & Related papers  (2022-10-31T14:06:11Z)
• Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers [12.09273192079783]
  We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We design novel computationally efficient algorithms for the realizable setting that match the minimax lower bound up to logarithmic factors.
  arXiv  Detail & Related papers  (2021-11-09T02:33:36Z)
• Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems [0.6882042556551609]
  This approach makes use of a algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees.
  arXiv  Detail & Related papers  (2020-10-22T08:33:58Z)
• Differentially Private Clustering: Tight Approximation Ratios [57.89473217052714]
  We give efficient differentially private algorithms for basic clustering problems. Our results imply an improved algorithm for the Sample and Aggregate privacy framework. One of the tools used in our 1-Cluster algorithm can be employed to get a faster quantum algorithm for ClosestPair in a moderate number of dimensions.
  arXiv  Detail & Related papers  (2020-08-18T16:22:06Z)
• Corralling Stochastic Bandit Algorithms [54.10645564702416]
  We show that the regret of the corralling algorithms is no worse than that of the best algorithm containing the arm with the highest reward. We show that the gap between the highest reward and other rewards depends on the gap between the highest reward and other rewards.
  arXiv  Detail & Related papers  (2020-06-16T15:33:12Z)
• Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
  We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
  arXiv  Detail & Related papers  (2020-01-29T09:40:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.