Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Computational Statistics & Data Analysis - Special issue on classification
On finding the number of clusters
Pattern Recognition Letters
Finding salient regions in images: nonparametric clustering for image segmentation and grouping
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining changing customer segments in dynamic markets
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A change detection method for sequential patterns
Decision Support Systems
Classifying the segmentation of customer value via RFM model and RS theory
Expert Systems with Applications: An International Journal
A transaction pattern analysis system based on neural network
Expert Systems with Applications: An International Journal
Intelligent profitable customers segmentation system based on business intelligence tools
Expert Systems with Applications: An International Journal
Mining changes in customer behavior in retail marketing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Behavior scoring model for coalition loyalty programs by using summary variables of transaction data
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The segmentation of customers is crucial for an organization wishing to develop appropriate promotion strategies for different clusters. Clustering customers provides an in-depth understanding of their behavior. However, previous studies have paid little attention to the similarity of different items in transaction. Lack of categories and concept levels of items, results from item-based segmentation methods are not as good as expected. Through employing a concept hierarchy of items, this study proposes a segmentation methodology to identify similarities between customers. First, the dissimilarity between transaction sequences is defined. Second, we adopt hierarchical clustering method to segment customers by their transaction data with concept hierarchy of consumed items. After segmentation, three cluster validation indices are used for optimizing the number of clusters of customers. Through the compassion of normalized index, the segmentation method proposed by this study rendered better results than other traditional methods.