scholarly journals Measuring Constraint-Set Utility for Partitional Clustering Algorithms

2006 ◽  
pp. 115-126 ◽  
Author(s):  
Ian Davidson ◽  
Kiri L. Wagstaff ◽  
Sugato Basu
Keyword(s):  
Clustering Algorithms ◽  
Partitional Clustering ◽  
Constraint Set

Integrating multi-criteria decision making and clustering for business customer segmentation

2015 ◽  
Vol 115 (6) ◽  
pp. 1022-1040 ◽  
Author(s):  
Hülya Güçdemir ◽  
Hasan Selim
Keyword(s):  
Decision Making ◽  
Clustering Algorithms ◽  
Customer Segmentation ◽  
Database Management System ◽  
Multi Criteria Decision Making ◽  
Business Markets ◽  
Content Type ◽  
Partitional Clustering ◽  
Rfm Model ◽  
Business Customers

Purpose – The purpose of this paper is to develop a systematic approach for business customer segmentation. Design/methodology/approach – This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely “Ward’s method,” “single linkage” and “complete linkage,” and a partitional clustering algorithm, “k-means,” are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments. Findings – Business customers of an international original equipment manufacturer (OEM) are segmented in the application. In this regard, 317 business customers of the OEM are segmented as “best,” “valuable,” “average,” “potential valuable” and “potential invaluable” according to the cluster ranks obtained in this study. The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. Research limitations/implications – The success of the proposed approach relies on the availability and quality of customers’ data. Therefore, design of an extensive customer database management system is the foundation for any successful customer relationship management (CRM) solution offered by the proposed approach. Such a database management system may entail a noteworthy level of investment. Practical implications – The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. By making customer segmentation decisions, the proposed approach can provides firms a basis for the development of effective loyalty programs and design of customized strategies for their customers. Social implications – The proposed segmentation approach may contribute firms to gaining sustainable competitive advantage in the market by increasing the effectiveness of CRM strategies. Originality/value – This study proposes an integrated approach for business customer segmentation. The proposed approach differentiates itself from its counterparts by combining MCDM and clustering in business customer segmentation. In addition, it extends the traditional RFM model by including five novel segmentation variables for business markets.

An Initialization Method for Clustering Mixed Numeric and Categorical Data Based on the Density and Distance

2015 ◽  
Vol 29 (07) ◽  
pp. 1550024 ◽  
Author(s):  
Jinchao Ji ◽  
Wei Pang ◽  
Yanlin Zheng ◽  
Zhe Wang ◽  
Zhiqiang Ma
Keyword(s):  
Real World ◽  
Mixed Type ◽  
Clustering Algorithms ◽  
Numerical Data ◽  
Mixed Data ◽  
Single Type ◽  
Partitional Clustering ◽  
Initial Cluster ◽  
Real World Datasets ◽  
Type Data

Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, we propose a novel initialization method for mixed data clustering. In the proposed method, both the distance and density are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional initialization methods.

scholarly journals Leaf Segmentation of Rosette Plants using Rough K-Means in CIELab Color Space

2021 ◽  
Author(s):  
Arunita Das ◽  
Daipayan Ghosal ◽  
Krishna Gopal Dhal
Keyword(s):  
Clustering Algorithms ◽  
Color Space ◽  
Ground Truth ◽  
Accurate Analysis ◽  
Partitional Clustering ◽  
Modern Agriculture ◽  
Cielab Color Space ◽  
Fuzzy C Means Clustering ◽  
Leaf Segmentation ◽  
Cielab Color

Segmentation of Plant Images plays an important role in modern agriculture where it can provide accurate analysis of a plant’s growth and possi-ble anomalies. In this paper, rough set based partitional clustering technique called Rough K-Means has been utilized in CIELab color space for the proper leaf segmentation of rosette plants. The eÿcacy of the proposed technique have been analysed by comparing it with the results of tra-ditional K-Means and Fuzzy C-Means clustering algorithms. The visual and numerical results re-veal that the RKM in CIELab provides the near-est result to the ideal ground truth, hence the most eÿcient one.

scholarly journals An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Clustering Algorithms ◽  
Real Life ◽  
Swarm Optimization ◽  
Local Optima ◽  
Partitional Clustering ◽  
Cat Swarm Optimization ◽  
Local Searches ◽  
Velocity Equation ◽  
Data Objects ◽  
Global And Local

Clustering is an unsupervised machine learning technique that optimally organizes the data objects in a group of clusters. In present work, a meta-heuristic algorithm based on cat intelligence is adopted for optimizing clustering problems. Further, to make the cat swarm algorithm (CSO) more robust for partitional clustering, some modifications are incorporated in it. These modifications include an improved solution search equation for balancing global and local searches, accelerated velocity equation for addressing diversity, especially in tracing mode. Furthermore, a neighborhood-based search strategy is introduced to handle the local optima and premature convergence problems. The performance of enhanced cat swarm optimization (ECSO) algorithm is tested on eight real-life datasets and compared with the well-known clustering algorithms. The simulation results confirm that the proposed algorithm attains the optimal results than other clustering algorithms.

scholarly journals K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021 ◽  
Vol 11 (23) ◽  
pp. 11246
Author(s):  
Abiodun M. Ikotun ◽  
Mubarak S. Almutari ◽  
Absalom E. Ezugwu
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimization Techniques ◽  
Background Information ◽  
Cluster Number ◽  
Automatic Data ◽  
Partitional Clustering ◽  
Automatic Clustering ◽  
Clustering Problems

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

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