Mining of mixed data with application to catalog marketing

Hsu, Chung-Chian; Chen, Yu‐Cheng

doi:10.1016/j.eswa.2005.11.017

Cited by 72 publications

(38 citation statements)

References 14 publications

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“…The case of our study, where represented papers in the citation network is indicated in a group with same research field, a cluster can be identified, into the cluster the density of edge and arrow is high. There are many fields that clustering algorithm extended in them, for example genetic engineering, customer segmentation and publication analysis, normally these type data are numerical and classified attributes (Hsu & Chen, 2007). The optimization of modularity tools in Gephi is under Louvain method, an iterative method to optimized modularity as the algorithm progresses.…”

Section: Publication Classification With Clustering Methodsmentioning

confidence: 99%

Optimization in supply chain management, the current state and future directions: A systematic review and bibliometric analysis

Movahedipour

Yang

Zeng

et al. 2016

JIEM

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Abstract:Purpose: The purpose of this paper is finding the current state of research and identifies high-potential area for future investigation in optimization in supply chain management.Design/methodology/approach: In this paper we present Bibliometric and Network analysis to examine current state research on optimization in supply chain management to identify established and emergent research field for future investigation. The systematic research review which we used in our study have not grasp or assess by other researchers on this topic. Firstly, based on our methodology Bibliometric analysis began by identifying 1610 publications raised from scientific journals, included literatures from 1994 to March of 2016. Secondly, we applied PageRank algorithm in our data for citation analysis to indicate the significance of a publication.Thirdly, the topological decision variables analysis is done based on Louvain method for network data clustering, for this proposes we used the rigorous tools.Finding: Based on our Network analysis result, the optimization in supply chain management research can be divided into four clusters/modules that introduced fundamental skill, knowledge, theory, application and method.-933-Journal of Industrial Engineering and Management -http://dx.doi. org/10.3926/jiem.2035 Research limitations/implications: We presented some limitation in our research in some fields which could allow new researchers and practitioners conduct the future research to grow up in different dimensions. Practical implications:Practitioners or policy maker usually are not familiar with these type researches so this is why mush of these survey remain in theatrical and conceptual. Future investigation needs to play in practical application domain instead stop merely in opinion.Originality/value: Based on our research, the researchers have more attention to work in conceptual analysis due to other fields but we believe that in facility location problem there many remarkable rooms still exist for future research to development. We also contributed more details in the papers.

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Section: Publication Classification With Clustering Methodsmentioning

confidence: 99%

Optimization in supply chain management, the current state and future directions: A systematic review and bibliometric analysis

Movahedipour

Yang

Zeng

et al. 2016

JIEM

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“…There are two approaches of attribute conversion [8]. One is that categorical values are converted to numeric values.…”

Section: Related Workmentioning

confidence: 99%

An Improved Kernel Clustering Algorithm for Mixed-Type Data in Network Forensic

Ren¹,

Liu²,

Wang³

et al. 2016

IJSIA

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“…For mixed data clustering, in addition to using 1-hot encoding to obtain continuous features or Gower's coefficient [Gower, 1971] and its extensions [Legendre and Legendre, 1998;Podani, 1999] to measure the similarities between data points, as introduced in Section 1, there are also some specially designed clustering algorithms, including k-prototypes [Huang, 1997;], K-means-mixed [Ahmad and Dey, 2007], CAVE [Hsu and Chen, 2007], M-ART [Hsu and Huang, 2008], INTEGRATE [Böhm et al, 2010], INCONCO [Plant and Böhm, 2011], SCENIC [Plant, 2012] and so on. K-prototypes algorithm [Huang, 1997;], which essentially follows the same idea of k-means algorithm, calculates the dissimilarity between two mixed-type objects as a combination of the squared Euclidean distance measure on the numeric attributes and the simple matching dissimilarity measure on the categorical attributes.…”

Section: Related Workmentioning

confidence: 99%

“…This idea of computing distances "globally" is similar to ours, but it's only applied within categorical attributes. CAVE [Hsu and Chen, 2007] uses variance to measure the similarity of the numeric part of the data and computes the similarity of the categorical part based on entropy weighted by the distances in the hierarchies. Similarly, the incremental clustering algorithm M-ART [Hsu and Huang, 2008] also computes the distance between two data points according to distance hierarchies associated with the mixed-type attributes.…”

Section: Related Workmentioning

confidence: 99%

Affinity Learning for Mixed Data Clustering

Latecki

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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In this paper, we propose a novel affinity learning based framework for mixed data clustering, which includes: how to process data with mixed-type attributes, how to learn affinities between data points, and how to exploit the learned affinities for clustering. In the proposed framework, each original data attribute is represented with several abstract objects defined according to the specific data type and values. Each attribute value is transformed into the initial affinities between the data point and the abstract objects of attribute. We refine these affinities and infer the unknown affinities between data points by taking into account the interconnections among the attribute values of all data points. The inferred affinities between data points can be exploited for clustering. Alternatively, the refined affinities between data points and the abstract objects of attributes can be transformed into new data features for clustering. Experimental results on many real world data sets demonstrate that the proposed framework is effective for mixed data clustering.

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Mining of mixed data with application to catalog marketing

Cited by 72 publications

References 14 publications

Optimization in supply chain management, the current state and future directions: A systematic review and bibliometric analysis

Optimization in supply chain management, the current state and future directions: A systematic review and bibliometric analysis

An Improved Kernel Clustering Algorithm for Mixed-Type Data in Network Forensic

Affinity Learning for Mixed Data Clustering

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