Multi-objective Consensus Clustering Framework for Flight Search Recommendation

• URL: http://arxiv.org/abs/2002.10241v2
• Date: Wed, 26 Feb 2020 14:41:59 GMT
• Title: Multi-objective Consensus Clustering Framework for Flight Search Recommendation
• Authors: Sujoy Chatterjee, Nicolas Pasquier, Simon Nanty, Maria A. Zuluaga
• Abstract summary: Clustering ensemble approaches were developed to overcome well-known problems of classical clustering approaches. We present a new clustering ensemble multi-objective optimization-based framework developed for analyzing Amadeus customer search data.
• Score: 4.5782961896413035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the travel industry, online customers book their travel itinerary according to several features, like cost and duration of the travel or the quality of amenities. To provide personalized recommendations for travel searches, an appropriate segmentation of customers is required. Clustering ensemble approaches were developed to overcome well-known problems of classical clustering approaches, that each rely on a different theoretical model and can thus identify in the data space only clusters corresponding to this model. Clustering ensemble approaches combine multiple clustering results, each from a different algorithmic configuration, for generating more robust consensus clusters corresponding to agreements between initial clusters. We present a new clustering ensemble multi-objective optimization-based framework developed for analyzing Amadeus customer search data and improve personalized recommendations. This framework optimizes diversity in the clustering ensemble search space and automatically determines an appropriate number of clusters without requiring user's input. Experimental results compare the efficiency of this approach with other existing approaches on Amadeus customer search data in terms of internal (Adjusted Rand Index) and external (Amadeus business metric) validations.

Related papers

• From A-to-Z Review of Clustering Validation Indices [4.08908337437878]
  We review and evaluate the performance of internal and external clustering validation indices on the most common clustering algorithms. We suggest a classification framework for examining the functionality of both internal and external clustering validation measures.
  arXiv  Detail & Related papers  (2024-07-18T13:52:02Z)
• CLAMS: A System for Zero-Shot Model Selection for Clustering [3.7127285734321194]
  We propose an AutoML system that enables model selection on clustering problems by leveraging optimal transport-based dataset similarity. We compare our results against multiple clustering baselines and find that it outperforms all of them, hence demonstrating the utility of similarity-based automated model selection for solving clustering applications.
  arXiv  Detail & Related papers  (2024-07-15T23:50:07Z)
• A3S: A General Active Clustering Method with Pairwise Constraints [66.74627463101837]
  A3S features strategic active clustering adjustment on the initial cluster result, which is obtained by an adaptive clustering algorithm. In extensive experiments across diverse real-world datasets, A3S achieves desired results with significantly fewer human queries.
  arXiv  Detail & Related papers  (2024-07-14T13:37:03Z)
• Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model [79.46465138631592]
  We devise an efficient algorithm that recovers clusters using the observed labels. We present Instance-Adaptive Clustering (IAC), the first algorithm whose performance matches these lower bounds both in expectation and with high probability.
  arXiv  Detail & Related papers  (2023-06-18T08:46:06Z)
• Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation [65.268245109828]
  Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the encoder maps all inputs to the same point and all are put into a single cluster. We propose a method that does not require data augmentation, and that, differently from existing methods, regularizes the hard assignments.
  arXiv  Detail & Related papers  (2023-03-29T08:23:26Z)
• Clustering Optimisation Method for Highly Connected Biological Data [0.0]
  We show how a simple metric for connectivity clustering evaluation leads to an optimised segmentation of biological data. The novelty of the work resides in the creation of a simple optimisation method for clustering crowded data.
  arXiv  Detail & Related papers  (2022-08-08T17:33:32Z)
• On the Convergence of Clustered Federated Learning [57.934295064030636]
  In a federated learning system, the clients, e.g. mobile devices and organization participants, usually have different personal preferences or behavior patterns. This paper proposes a novel weighted client-based clustered FL algorithm to leverage the client's group and each client in a unified optimization framework.
  arXiv  Detail & Related papers  (2022-02-13T02:39:19Z)
• Personalized Federated Learning via Convex Clustering [72.15857783681658]
  We propose a family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized.
  arXiv  Detail & Related papers  (2022-02-01T19:25:31Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)

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.