Hybrid soft computing approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customer-centric industries

Khalili‐Damghani, Kaveh; Abdi, Fatemeh; Abolmakarem, Shaghayegh

doi:10.1016/j.asoc.2018.09.001

Cited by 56 publications

(24 citation statements)

References 68 publications

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“…The missing values regarding daily payment and penalty transactions for these participants are replaced with the best score of '5'. (see [19]). In its simplest form, K-means starts by se- DBSCAN algorithm can find clusters of any shape, as opposed to K-means algorithm which assumes that the clusters are convex-shaped.…”

Section: Handling Missing Valuesmentioning

confidence: 96%

Clustering electricity market participants via FRM models

Gülcü¹,

Çalişkan²

2021

IDT

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Collateral mechanism in the Electricity Market ensures the payments are executed on a timely manner; thus maintains the continuous cash flow. In order to value collaterals, Takasbank, the authorized central settlement bank, creates segments of the market participants by considering their short-term and long-term debt/credit information arising from all market activities. In this study, the data regarding participants’ daily and monthly debt payment and penalty behaviors is analyzed with the aim of discovering high-risk participants that fail to clear their debts on-time frequently. Different clustering techniques along with different distance metrics are considered to obtain the best clustering. Moreover, data preprocessing techniques along with Recency, Frequency, Monetary Value (RFM) scoring have been used to determine the best representation of the data. The results show that Agglomerative Clustering with cosine distance achieves the best separated clustering when the non-normalized dataset is used; this is also acknowledged by a domain expert.

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Section: Handling Missing Valuesmentioning

confidence: 96%

Clustering electricity market participants via FRM models

Gülcü¹,

Çalişkan²

2021

IDT

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“…These techniques generally require a subjective assessment for the necessary parameters. For this reason, data mining methods, such as K-means or Artificial Neural Network (ANN) clustering, can give more objective results for RFM customer segmentation [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, classification data mining methods have become very popular, as they can, based on the characteristics of the customer, enable the prediction to which segment the customer belongs to [15,16]. Since the most valuable customer segment is usually the smallest, there is a problem of class imbalance.…”

Section: Introductionmentioning

confidence: 99%

Class balancing in customer segments classification using support vector machine rule extraction and ensemble learning

Rogić

Kašćelan

2021

COMSIS J

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An objective and data-based market segmentation is a precondition for efficient targeting in direct marketing campaigns. The role of customer segments classification in direct marketing is to predict the segment of most valuable customers who is likely to respond to a campaign based on previous purchasing behavior. A good-performing predictive model can significantly increase revenue, but also, reduce unnecessary marketing campaign costs. As this segment of customers is generally the smallest, most classification methods lead to misclassification of the minor class. To overcome this problem, this paper proposes a class balancing approach based on Support Vector Machine-Rule Extraction (SVM-RE) and ensemble learning. Additionally, this approach allows for rule extraction, which can describe and explain different customer segments. Using a customer base from a company?s direct marketing campaigns, the proposed approach is compared to other data balancing methods in terms of overall prediction accuracy, recall and precision for the minor class, as well as profitability of the campaign. It was found that the method performs better than other compared class balancing methods in terms of all mentioned criteria. Finally, the results confirm the superiority of the ensemble SVM method as a preprocessor, which effectively balances data in the process of customer segments classification

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“…The dependence of advanced countries on petroleum products, especially gasoline and the outbreak of petroleum crises in the 1970 s due to the increasing and overcoming consumption on production and extraordinary rise in petroleum prices and its products, urged industrialized countries to deal with energy problem and take a different approach [1][2][3][4]. Undoubtedly, the importance of the strategic product, gasoline in terms of current conditions, quality and quantity dimensions is not covered on everyone with respect to the growing demand of the country and the growth of imports and further dependence and the spread of environmental pollution and the prevalence of various diseases caused by pollution and the low quality of existence gasoline [5][6][7].…”

Section: Introductionmentioning

confidence: 99%