An intelligent market segmentation system using k-means and particle swarm optimization

Chiu, Chih-Hui; Chen, Yi-Feng; Kuo, I-Ting; He, Kai

doi:10.1016/j.eswa.2008.05.029

Cited by 100 publications

(38 citation statements)

References 9 publications

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“…It offers benefits such as providing opportunities to expand the market by better satisfying the needs of particular visitors, increasing profitability and effectiveness, fine tuning product offerings and suggesting appropriate distribution and communication channels (Chiu, Chen, Kuo, & Ku, 2009). Other benefits include: clear definition of the visitor (Arrimond & Elfessi, 2001;Fogliatto & Silveira, 2008;Rogerson, & Kotze, 2011), better understanding of the market based on motivation (Alebaki & Iakovidou, 2011;Park & Yoon, 2009;Van Der Wagen, 2005), ability to respond to the markets' changing needs (Farrell & Simpson, 2008;Ferrell, Hartline, & Lucas, 2002;Pegg & Patterson, 2010), identification of niche markets (Brown & Cave, 2010;Getz, 1997;Keller & Kotler, 2006;Park & Yoon, 2009), increasing the cost effectiveness of marketing (Moeller, Dolnicar, & Leisch, 2011;Shin, 2008), innovative ideas (Van Der Wagen, 2005), understanding and assessing the competition (Burke & Resnick, 2000;Dibb & Simkin, 2001;Dolnicar, 2008;Tsiotsou & Ratten, 2010) and developing a sustainable and better positioned product (Bennett & Strydom, 2001;Lee, Lee, & Wicks, 2004;Luo & Deng, 2008;Moeller et al, 2011).…”

Section: Market Segmentation Of Wildflower Viewersmentioning

confidence: 98%

Who pays to view wildflowers in South Africa?

Kruger

Viljoen

Saayman

2013

Journal of Ecotourism

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This research identified viable target markets at two national parks in South Africa, using market segmentation based on demographic characteristics. We surveyed international, domestic and local wildflower-viewing visitors and labelled three distinct demographic clusters with appropriate flower names: Namaqualand Daisies, Gazanias and Wild Flaxes. Our results show that such clustering is a useful research tool for producing a clear visitor profile. It enabled us to provide strategic insights for managing wildflower viewing and similar natural events, according to the preferences of specific market segments.

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Section: Market Segmentation Of Wildflower Viewersmentioning

confidence: 98%

Who pays to view wildflowers in South Africa?

Kruger

Viljoen

Saayman

2013

Journal of Ecotourism

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“…Iris plant is a database with 4 numeric attributes, 3 classes and 150 instances. We compared the PSHBMO algorithm with PSO+K-means and SOM+K-means by Chiu [13]. Finally, we provided the clustering results and used three criteria to evaluate the quality of the results as in following table.…”

Section: Experiment-irismentioning

confidence: 99%

A Market Segmentation System for Consumer Electronics Industry Using Particle Swarm Optimization and Honey Bee Mating Optimization

Chiu

Kuo

Chen

2009

Global Perspective for Competitive Enterprise, Economy and Ecology

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Abstract. The use of information technologies in various business areas is emerging in recent years. With the development of information technology, how to find useful information existed in vast data has become an important issue. The most broadly discussed technique is data mining, which has been successfully applied to many fields and analytic tools. Clustering analysis which tries to segment data into homogeneous clusters is one of the most useful technologies in data mining methods. Market segmentation is among the important issue of most companies. Market segmentation relies on the data clustering in a huge data set. In this study, we propose a clustering system which integrated particle swarm optimization and honey bee mating optimization methods. Simulations for a benchmark test functions show that our proposed method possesses better ability to find the global optimum than other well-known clustering algorithms. The results show that system through PSHBMO can effectively find the global optimum solution, and extend the application of market segmentation to solve the RFM model.

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“…K-means realiza una búsqueda local en la vecindad de la solución inicial y va refinando la partición resultante, por esta razón se puede utilizar algún algoritmo de búsqueda global para generar los centroides iniciales. El algoritmo de cúmulo de partículas (PSO -Particle Swarm Optimization) es una técnica de optimización estocástica que puede utilizarse para encontrar una soluciónóptima o cercana alóptimo, ha sido aplicado en clustering de datos y de texto con muy buenos resultados [6], [10], [3]. PSO puede utilizarse para generar buenos centroides iniciales para el Kmeans.…”

Section: Introductionunclassified

An´alisis de medidas no-supervisadas de calidad en clusters obtenidos por K-means y Particle Swarm Optimization

Villagra

Guzmán

Pandolfi

et al. 2009

Ciencia Y Tecnología

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Data clustering helps in discerning the structure and simplifing the complexity of massive quantities of data. It is a common technique used in many fields, including machine learning, data mining, image analysis, and bioinformatics, in which the distribution of information can be of any size and shape. The efficiency of clustering algorithms is strongly required with very large databases and high-dimensional data types. This paper presents an evaluation study, from different perspectives, of several important unsupervised quality measures including quantization error, intra-and inter-cluster distances, obtained by the well-known K-means algorithm and a population-based metaheuristic called Particle Swarm Optimization (PSO) and a hybrid algorithm that combines the characteristics of both algorithms, called PSO+K-means. Results show that in general the PSO+K-means algorithm obtains better results in each measure and generates higher compact and separates clustering than either PSO or K-means alone.Keywords: Clustering, K-means, Particle Swarm Optimization, Unsupervised quality measures. ResumenEl clustering de datos ayuda a discernir la estructura y simplifica la complejidad de cantidades masivas de datos. Es una técnica común y se utiliza en diversos campos como, aprendizaje de máquina, minería de datos, reconocimiento de patrones, análisis de imágenes y bioinformática, donde la distribución de la información puede ser de cualquier tamaño y forma. La eficiencia de los algoritmos de clustering es extremadamente necesaria cuando se trabaja con enormes bases de datos y tipos de datos de grandes dimensiones. Este trabajo presenta una evaluación desde distintas perspectivas de una serie de medidas relevantes no-supervisadas de calidad como por ejemplo, cuantización del error, distancias intra-e inter-cluster, de los clusters obtenidos por el conocido algoritmo de K-means, una metaheurística poblacional denominada Particle Swarm Optimization (PSO) y un algoritmo híbrido, que combina las características de los dos algoritmos anteriores, denominado PSO+Kmeans. De los resultados obtenidos se observa que en general el algoritmo PSO+K-means obtiene mejores resultados en cada una de las medidas generando clusters más compactos y separados entre ellos que los obtenidos por los otros algoritmos.

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An intelligent market segmentation system using k-means and particle swarm optimization

Cited by 100 publications

References 9 publications

Who pays to view wildflowers in South Africa?

Who pays to view wildflowers in South Africa?

A Market Segmentation System for Consumer Electronics Industry Using Particle Swarm Optimization and Honey Bee Mating Optimization

An´alisis de medidas no-supervisadas de calidad en clusters obtenidos por K-means y Particle Swarm Optimization

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