A Novel Mixed Integer Linear Programming Model for Clustering Relational Networks

Pirim, Harun; Ekşioğlu, Burak; Glover, Fred

doi:10.1007/s10957-017-1213-1

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…Later, Pirim et al suggested an improved fuzzy objective function KLFCM. 20 Greedy-based method The greedy method for community detection is grouped into two categories: traditional and new greedy methods. In recent years, new greedy-based methods have been applied, such as heuristic algorithms, crowd-based greedy methods etc.…”

Section: Clustering Methodsmentioning

confidence: 99%

“…Pirim is measured by the number of links that connect these two clusters. Therefore, we present the integerprogramming model for community detection for advertising based on the proposed method of Pirim et al 4,20 We present an Integer programming model with a clustering approach. The objective function illustrates the definition of clustering.…”

Section: Problem Definition By Integer Programming Approachmentioning

confidence: 99%

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Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by the density-based clustering algorithm with a new distance function. The proposed method can improve clustering precision to detect communities by the futuristic greedy approach. We implemented the proposed algorithm with the map-reduce technique in the Hadoop structure. Experimental results in datasets show that the average of the rand index of clusters was accomplished by 99.32% in the proposed method. In addition, these results illustrate that there is a reduction in execution time by the proposed algorithm.

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Section: Clustering Methodsmentioning

confidence: 99%

Section: Problem Definition By Integer Programming Approachmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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“…Clusters are typically generated by integer programming formulations that either maximize the similarities among objects within a cluster or minimize the similarities between clusters. Compact or homogeneous clusters are generated by maximizing item similarities within clusters, whereas well-separated clusters are generated by decreasing similarities between distinct clusters (Pirim et al, 2018). Subject to certain unique clustering problem limitations, integer programming methods offer flexibility in expressing objectives.…”

Section: Introductionmentioning

confidence: 99%

Examination of Provinces in Türkiye about Sectoral Employment Share by Cluster Analysis

Bitgen Sungur,

Madenoğlu

2024

Anadolu Üniversitesi Sosyal Bilimler Dergisi

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The significance of regional dynamics in the process of economic development and regional development has increased as a result of significant factors like competitiveness, human resource development, and observation of the global market. In this study, mathematical programming-based cluster analysis has been conducted to group the regions in Türkiye according to sectoral employment rates. A mixed integer mathematical model is presented that maximizes the smallest of the out-of-cluster distances while minimizing the largest within-cluster distance. Level 2- 26 sub-regions in Türkiye are clustered according to sectoral employment data for 2021 and 2022. As a result, two clusters were obtained for both years in our country according to employment status by gender on a sectoral basis. One of these clusters is where the employment rate of the agricultural sector is higher than other sectors, and the other is where the employment rate of the industrial and service sectors is higher. When the 2021 and 2022 clusters are compared, in total, TR22, TR32, TR33, TRC3; in men, TR21, TR22, TR32, TR52, TR81; In women, it was observed that TRC1 regions were assigned to different clusters. By implementing a successful employment policy as human resource development across the national government, it will be possible to ensure the balanced growth of provinces located in Türkiye's various geographical areas.

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“…A few approaches were proposed for slight variations of CPP, like branchand-price for the capacitated version [32] or branch-and-price-and-cut method for CPP with minimum clique size requirement [33]. Integer programming models for clustering proved to be a useful tool [34]. And the vast majority of approaches try to solve CPP formulated as an integer linear programming (ILP) problem [3,9,30].…”

Section: Introductionmentioning

confidence: 99%

Subnetwork Constraints for Tighter Upper Bounds and Exact Solution of the Clique Partitioning Problem

Belyi,

Sobolevsky,

Kurbatski

et al. 2021

Preprint

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We consider a variant of the clustering problem for a complete weighted graph. The aim is to partition the nodes into clusters maximizing the sum of the edge weights within the clusters. This problem is known as the clique partitioning problem, being NP-hard in the general case of having edge weights of different signs. We propose a new method of estimating an upper bound of the objective function that we combine with the classical branch-and-bound technique to find the exact solution. We evaluate our approach on a broad range of random graphs and real-world networks. The proposed approach provided tighter upper bounds and achieved significant convergence speed improvements compared to known alternative methods.

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A Novel Mixed Integer Linear Programming Model for Clustering Relational Networks

Cited by 14 publications

References 19 publications

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Examination of Provinces in Türkiye about Sectoral Employment Share by Cluster Analysis

Subnetwork Constraints for Tighter Upper Bounds and Exact Solution of the Clique Partitioning Problem

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