Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Panapakidis, Ioannis P.; Christoforidis, Georgios C.

doi:10.3390/app8020237

Cited by 30 publications

(13 citation statements)

References 86 publications

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“…supplementary material available on line). Such an MCDA approach has already been applied either in comparative analyses of clustering algorithms [26,27] or to propose an integrated clustering validity measure [28].…”

Section: Cluster Validationmentioning

confidence: 99%

“…However, when using a large number of quality indicators, the Pareto front can include a large number of solutions. In this case, it may be useful to complement the analysis with a classical multicriteria analysis to rank the solutions, as in [26,27]. This type of analysis allows to omit small differences between index values and to limit the impact of very large differences by using piecewise linear preference functions (cf.…”

Section: World Happiness Report 2019 Datasetmentioning

confidence: 99%

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Assessing partially ordered clustering in a multicriteria comparative context

Rosenfeld

Smet

Debeir

et al. 2021

Pattern Recognition

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This study considers the task of clustering for data characterized by peculiar quantitative features in that they express performance according to different indicators or criteria. Performance is supposed to be optimized in one way or the other, i.e. maximized or minimized. This peculiar type of data introduces a comparative context that is not generally taken into account in the field of pattern recognition, in general, and clustering, in particular. In the present study, we introduce different concepts and develop tools that facilitate the evaluation of data partitions in this comparative context leading to the consideration of asymmetric preference relationships between objects and between clusters. We show their usefulness on the basis of artificial data and also by analyzing the results produced on real data by means of clustering methods.

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Section: Cluster Validationmentioning

confidence: 99%

Section: World Happiness Report 2019 Datasetmentioning

confidence: 99%

Assessing partially ordered clustering in a multicriteria comparative context

Rosenfeld

Smet

Debeir

et al. 2021

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…Multi-Criteria decision making (MCDM) is one of the most well-known branches of decision making [ 26 ]. MCDM is divided into multi-objective decision making (MODM) and multi-attribute decision making (MADM).…”

Section: Introductionmentioning

confidence: 99%

Developing a Multi-Criteria Decision Making approach to compare types of classroom furniture considering mismatches for anthropometric measures of university students

et al. 2020

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The mismatch between students’ anthropometric measures and school furniture dimensions have been investigated in many countries. In Iran, collegians spend at least a quarter of the day hours at university in the sitting position, so it is essential to evaluate furniture mismatch among university students. In Iranian universities, the use of chairs with an attached table is widespread, while the study of mismatches in these chairs among the collegian community is rare. This study was aimed to compare and rank different classroom furniture types based on the mismatch between collegians’ anthropometric measures and the dimensions of classroom furniture among Industrial Engineering students by developing a Multi-Criteria Decision Making approach in an integrated Methodology. The sample consisted of 111 participants (71 males, 40 females). Ten anthropometric measures were gathered, together with eight furniture dimensions for four types of chairs. Mismatch analyses were carried out using mismatch equations, and the Simple Additive Weighting method was used as a base method to solve the decision-making problem. The results indicated that Underneath Desk Height and Seat to Desk Clearance showed the highest levels of the match, while Seat Width presents the highest levels of low mismatch. According to the results, Type 1 and Type 3 were the best current classroom furniture. The Sensitivity Analysis was performed in two ways: changing the weights of criteria in nine scenarios and comparing the results with five other MCDM methods. The proposed MCDM approach can be used widely in furniture procurement processes and educational environments.

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“…The objective of any clustering algorithm is to ensure that the distance between data points in the cluster is very low as compared to the distance to other clusters. In other words, members of a cluster are very similar, and members of different clusters are extremely dissimilar [13]. Clustering is common purposes in many fields such as cluster evaluation Economic Science, biology, medicine, business and marketing, computer science or social science, documents classification, pattern recognition, digital image Processing, textual content mining [14].…”

Section: Cluster Analysis Using K-medoid Algorithmmentioning

confidence: 99%

“…The field-specific by means of AUC represents the chance that a random pair of churning and non-churning customers are properly identified, i.e. A positive instance receives a greater rating than a negative instance [13].…”

Section: Confusion Matrixmentioning

confidence: 99%

Churners Prediction Based on Mining the Content of Social Network Taxonomy

2019

IJRTE

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Churner Customer is a main tricky and one of the most important issues for large companies, due to the straight impact on the incomes of the companies especially in the telecom domain, companies are searching for advance strategies to predict churn/non-churn customer. This research focuses on the construction of a predictive model to identify each customer as churner or not and gain additional insights about their service consumers. The main contribution is to overcome the limitation of independently based on data mining strategies by developing approaches and derived network metrics such as centrality and connectivity between customers to incorporate network mining with traditional data mining. Social network measurements e.g. Leverage, flow Bet, Page Rank, Cluster Coefficients and Eccentricity are joined with other attributes in the original network dataset to enhance the performance of the proposed methodology. The risk of churn can be predictive by preparing an extensive cleaning the raw data for churn modeling, It divides customers into clusters based on Gower distance and k-medoids algorithm to help understand and predict churner users, classification model using Extreme Gradient Boosting “XGBoost”, assessment the model performance by computation the centralities metrics as new attributes appended to the original network dataset. Experiments conducted on Telecom shows that with an average value of all statistics accuracy not lower than 98.27%, while the average accuracy for the original dataset with it is clusters is not exceeded than 0.97%. The proposed method for churners detection which combines social impacts and network contents based on clustering significantly improved the prediction accuracy for telecom dataset as compared to prediction using the call log details, network information without implement of clustering , thus validate the hypothesis that combining social network attributes and Call/SMS information of the users for churn prediction could yields substantially improved of customer churn prediction.

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Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Cited by 30 publications

References 86 publications

Assessing partially ordered clustering in a multicriteria comparative context

Assessing partially ordered clustering in a multicriteria comparative context

Developing a Multi-Criteria Decision Making approach to compare types of classroom furniture considering mismatches for anthropometric measures of university students

Churners Prediction Based on Mining the Content of Social Network Taxonomy

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