A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry
The hospitality industry is one of the data-rich industries that receives huge Volumes of data streaming at high Velocity with considerably Variety, Veracity, and Variability. These properties make the data analysis in the hospitality industry a big data problem. Meeting the customers' expectations is a key factor in the hospitality industry to grasp the customers' loyalty. To achieve this goal, marketing professionals in this industry actively look for ways to utilize their data in the best possible manner and advance their data analytic solutions, such as identifying a unique market segmentation clustering and developing a recommendation system. In this paper, we present a comprehensive literature review of existing big data clustering algorithms and their advantages and disadvantages for various use cases. We implement the existing big data clustering algorithms and provide a quantitative comparison of the performance of different clustering algorithms for different scenarios. We also present our insights and recommendations regarding the suitability of different big data clustering algorithms for different use cases. These recommendations will be helpful for hoteliers in selecting the appropriate market segmentation clustering algorithm for different clustering datasets to improve the customer experience and maximize the hotel revenue.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringMarketingSimilar Papers 제목 키워드 기반
Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data
This paper presents a comprehensive comparative analysis of prominent clustering algorithms K-means, DBSCAN, and Spectral Clustering on high-dimensional datasets. We introduce a novel evaluation framework that assesses c…
ClusteringComputational EfficiencyDimensionality ReductionClassifying Mental-Disorders through Clinicians Subjective Approach based on Three-way Decision
In psychiatric diagnosis, a contemporary data-driven, manual-based method for mental disorders classification is the most popular technique; however, it has several inevitable flaws. Using the three-way decision as a fra…
Consistent Representation Learning for High Dimensional Data Analysis
High dimensional data analysis for exploration and discovery includes three fundamental tasks: dimensionality reduction, clustering, and visualization. When the three associated tasks are done separately, as is often the…
ClusteringDimensionality ReductionRepresentation LearningVocal Bursts Intensity PredictionResearch on the application of graph data structure and graph neural network in node classification/clustering tasks
Graph-structured data are pervasive across domains including social networks, biological networks, and knowledge graphs. Due to their non-Euclidean nature, such data pose significant challenges to conventional machine le…
Graph Representation LearningGraph Neural NetworkNode ClassificationKnowledge Graphs3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images
Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales. Contemporary work primarily revolves a…
ClusteringImage SegmentationSegmentationSemantic Segmentation