Journal ArticleDOI
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
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TLDR
The paper compares conventional and non-conventional clustering techniques, as well as temporal andnon-temporal analysis of customer loyalty, and the interval set clustering is shown to provide an interesting dimension to a temporal analysis.About:
This article is published in Information Sciences.The article was published on 2005-06-01. It has received 61 citations till now. The article focuses on the topics: Cluster analysis & Rough set.read more
Citations
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Developing recommender systems with the consideration of product profitability for sellers
TL;DR: Comparisons between the proposed CPPRS and HPRS systems and traditional recommender systems are made to clarify the advantages and disadvantages of these systems in terms of recommendation accuracy and/or profit from cross-selling.
Journal ArticleDOI
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
TL;DR: This article compares k-mean to fuzzy c-means and rough k-Means as important representatives of soft clustering, and surveys important extensions and derivatives of these algorithms.
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A hybrid of sequential rules and collaborative filtering for product recommendation
TL;DR: This work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentations-based KNN-CF method, and shows that the hybrid method outperforms traditional CF methods.
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Hierarchical clustering of mixed data based on distance hierarchy
TL;DR: A distance representation scheme, distance hierarchy, is proposed, which facilitates expressing the similarity between categorical values and also unifies distance measuring of numerical and categoricalvalues.
Journal ArticleDOI
Mining spatial association rules in image databases
TL;DR: A novel spatial mining algorithm, called 9 DLT-Miner, to mine the spatial association rules from an image database, where every image is represented by the 9DLT representation, which is more efficient than the Apriori algorithm.
References
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Journal ArticleDOI
Rough sets
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Lecture Notes in Artificial Intelligence
P. Brezillon,P. Bouquet +1 more
TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.
Self-organization and associative memory
TL;DR: In this paper, the problem of infinite-state memory is addressed in the context of biological memory using an adaptive filtering approach based on the classical laws of association, which is used for the purpose and nature of the biological memory.
Journal ArticleDOI
Relational interpretations of neighborhood operators and rough set approximation operators
TL;DR: This paper presents a framework for the formulation, interpretation, and comparison of neighborhood systems and rough set approximations using the more familiar notion of binary relations, and introduces a special class of neighborhood system, called 1-neighborhood systems.