Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.10055838
Pantea Parsi, Mohammad Khanbabaei, Najmeh Farhadi
{"title":"Using data mining to integrate Recency-Frequency-Monetary value (RFM) analysis and credit scoring methods for bank customer behavior analysis","authors":"Pantea Parsi, Mohammad Khanbabaei, Najmeh Farhadi","doi":"10.1504/ijdmmm.2023.10055838","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.10055838","url":null,"abstract":"","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82154330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An ABC approach for depression signs on social networks posts","authors":"Amina Madani, Fatima Boumahdi, Anfel Boukenaoui, Mohamed Chaouki Kritli, Asma Ghribi, Fatma Limani, Hamza Hentabli","doi":"10.1504/ijdmmm.2023.132972","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.132972","url":null,"abstract":"","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136257001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.134612
Muhammad Irfan Yousuf, Raheel Anwar
Graph sampling provides an efficient yet inexpensive solution for analysing large graphs. The purpose of sampling a graph is to extract a small representative subgraph from a big graph so that the sample can be used in place of the big graph for studying and analysing it. In this paper, we propose a new sampling method called weighted edge sampling. In this method, we give equal weight to all the edges in the beginning. During the sampling process, we sample an edge with the probability proportional to its weight. When an edge is sampled, we increase the weight of its neighbouring edges and this increases their probability to be sampled. Our method extracts the neighbourhood of a sampled edge more efficiently than previous approaches. We evaluate the efficacy of our sampling approach empirically using several real-world datasets. We find that our method produces better samples than the previous approaches. Our results show that our samples better estimate the degree and path length of the original graphs whereas our samples are less efficient in estimating the clustering coefficient of a graph.
{"title":"Weighted edge sampling for static graphs","authors":"Muhammad Irfan Yousuf, Raheel Anwar","doi":"10.1504/ijdmmm.2023.134612","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.134612","url":null,"abstract":"Graph sampling provides an efficient yet inexpensive solution for analysing large graphs. The purpose of sampling a graph is to extract a small representative subgraph from a big graph so that the sample can be used in place of the big graph for studying and analysing it. In this paper, we propose a new sampling method called weighted edge sampling. In this method, we give equal weight to all the edges in the beginning. During the sampling process, we sample an edge with the probability proportional to its weight. When an edge is sampled, we increase the weight of its neighbouring edges and this increases their probability to be sampled. Our method extracts the neighbourhood of a sampled edge more efficiently than previous approaches. We evaluate the efficacy of our sampling approach empirically using several real-world datasets. We find that our method produces better samples than the previous approaches. Our results show that our samples better estimate the degree and path length of the original graphs whereas our samples are less efficient in estimating the clustering coefficient of a graph.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135263329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.132981
Behnam Khamoushpour, Abbas Sheikh Aboumasoudi, Arash Shahin, Shakiba Khademolqorani
The purpose of this study is to select and rank the indicators affecting service quality and minimise the service quality gap. In this regards, two famous methods of meta-heuristic algorithms, one genetic algorithm and the other particle swarm optimisation, and their combination with support vector machine, namely 'GA-SVM and PSO-SVM' are used. Also, two macro quality indicators, including five performance indicators and five service quality gap indicators from the SERVQUAL model are considered. GA-SVM algorithm has been used to select the effective indicators in service quality and PSO-SVM has been implemented to rank these indicators. The efficiency and accuracy of the presented approach were confirmed through implementation on a manufacturing company. According to the obtained data, the two performance indicators of the final time of service level and the level of response do not play an important role in measuring and improving the quality of services provided in the company.
{"title":"Designing a model for selecting, ranking and optimising service quality indicators using meta-heuristic algorithms","authors":"Behnam Khamoushpour, Abbas Sheikh Aboumasoudi, Arash Shahin, Shakiba Khademolqorani","doi":"10.1504/ijdmmm.2023.132981","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.132981","url":null,"abstract":"The purpose of this study is to select and rank the indicators affecting service quality and minimise the service quality gap. In this regards, two famous methods of meta-heuristic algorithms, one genetic algorithm and the other particle swarm optimisation, and their combination with support vector machine, namely 'GA-SVM and PSO-SVM' are used. Also, two macro quality indicators, including five performance indicators and five service quality gap indicators from the SERVQUAL model are considered. GA-SVM algorithm has been used to select the effective indicators in service quality and PSO-SVM has been implemented to rank these indicators. The efficiency and accuracy of the presented approach were confirmed through implementation on a manufacturing company. According to the obtained data, the two performance indicators of the final time of service level and the level of response do not play an important role in measuring and improving the quality of services provided in the company.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136256972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.10055032
M. Kamel, Jassim Mohammed Dahr, Wid Akeel Awadh, Ali Salah Alasady, Alaa Khalaf Hamoud, Aqeel Majeed Humadi, I. A. Najm
{"title":"A Comparative Study of Supervised/Unsupervised Machine Learning Algorithms with Feature Selection Approaches to Predict Student Performance","authors":"M. Kamel, Jassim Mohammed Dahr, Wid Akeel Awadh, Ali Salah Alasady, Alaa Khalaf Hamoud, Aqeel Majeed Humadi, I. A. Najm","doi":"10.1504/ijdmmm.2023.10055032","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.10055032","url":null,"abstract":"","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"11 1","pages":"393-409"},"PeriodicalIF":0.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88482863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.132975
Wallace Anacleto Pinheiro, Ana Bárbara Sapienza Pinheiro
{"title":"Hierarchical++: improving the hierarchical clustering algorithm","authors":"Wallace Anacleto Pinheiro, Ana Bárbara Sapienza Pinheiro","doi":"10.1504/ijdmmm.2023.132975","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.132975","url":null,"abstract":"","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136256979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.10051983
K. Bedjou, F. Azouaou
{"title":"Detection of Terrorisms Apologies on Twitter using a New Bi-lingual Dataset","authors":"K. Bedjou, F. Azouaou","doi":"10.1504/ijdmmm.2023.10051983","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.10051983","url":null,"abstract":"","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"55 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87538207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.10059714
Muhammad Irfan Yousuf, Raheel Anwar
Graph Sampling provides an efficient yet inexpensive solution for analyzing large graphs. While extracting small representative subgraphs from large graphs, the challenge is to capture the properties of the original graph. Several sampling algorithms have been proposed in previous studies, but they lack in extracting good samples. In this paper, we propose a new sampling method called Weighted Edge Sampling. In this method, we give equal weight to all the edges in the beginning. During the sampling process, we sample an edge with the probability proportional to its weight. When an edge is sampled, we increase the weight of its neighboring edges and this increases their probability to be sampled. Our method extracts the neighborhood of a sampled edge more efficiently than previous approaches. We evaluate the efficacy of our sampling approach empirically using several real-world data sets and compare it with some of the previous approaches. We find that our method produces samples that better match the original graphs. We also calculate the Root Mean Square Error and Kolmogorov Smirnov distance to compare the results quantitatively.
{"title":"Weighted Edge Sampling for Static Graphs","authors":"Muhammad Irfan Yousuf, Raheel Anwar","doi":"10.1504/ijdmmm.2023.10059714","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.10059714","url":null,"abstract":"Graph Sampling provides an efficient yet inexpensive solution for analyzing large graphs. While extracting small representative subgraphs from large graphs, the challenge is to capture the properties of the original graph. Several sampling algorithms have been proposed in previous studies, but they lack in extracting good samples. In this paper, we propose a new sampling method called Weighted Edge Sampling. In this method, we give equal weight to all the edges in the beginning. During the sampling process, we sample an edge with the probability proportional to its weight. When an edge is sampled, we increase the weight of its neighboring edges and this increases their probability to be sampled. Our method extracts the neighborhood of a sampled edge more efficiently than previous approaches. We evaluate the efficacy of our sampling approach empirically using several real-world data sets and compare it with some of the previous approaches. We find that our method produces samples that better match the original graphs. We also calculate the Root Mean Square Error and Kolmogorov Smirnov distance to compare the results quantitatively.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.132974
Nouha Arfaoui
Healthcare domain generates huge amount of data from different and heterogynous clinical data sources using different devices to ensure a good managing hospital performance. Because of the quantity and complexity structure of the data, we use big healthcare data warehouse for the storage first and the decision making later. To achieve our goal, we propose a new process that deals with this type of data. It starts by unifying the different data, then it extracts it, loads it into big healthcare data warehouse and finally it makes the necessary transformations. For the first step, the ontology is used. It is the best solution to solve the problem of data sources heterogeneity. We use, also, Hadoop and its ecosystem including Hive, MapReduce and HDFS to accelerate the treatment through the parallelism exploiting the performance of ELT to ensure the 'schema-on-read' where the data is stored before performing the transformation tasks.
{"title":"A new process for healthcare big data warehouse integration","authors":"Nouha Arfaoui","doi":"10.1504/ijdmmm.2023.132974","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.132974","url":null,"abstract":"Healthcare domain generates huge amount of data from different and heterogynous clinical data sources using different devices to ensure a good managing hospital performance. Because of the quantity and complexity structure of the data, we use big healthcare data warehouse for the storage first and the decision making later. To achieve our goal, we propose a new process that deals with this type of data. It starts by unifying the different data, then it extracts it, loads it into big healthcare data warehouse and finally it makes the necessary transformations. For the first step, the ontology is used. It is the best solution to solve the problem of data sources heterogeneity. We use, also, Hadoop and its ecosystem including Hive, MapReduce and HDFS to accelerate the treatment through the parallelism exploiting the performance of ELT to ensure the 'schema-on-read' where the data is stored before performing the transformation tasks.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136256594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijdmmm.2023.132973
Mohammed El Amine Laghzaoui, Yahia Lebbah
Itemset mining is the first pattern mining problem studied in the literature. Most of the itemset mining studies have considered only Boolean datasets, where each transaction can contain or not items. In practical applications, items appear in some transactions with some quantities. In this paper, we propose an extension of the current efficient constraint programming approach for itemset mining, to take into account quantitative items in order to find patterns with their quantities directly on the original quantitative dataset. The contribution is two folds. Firstly, we facilitate the modelling task of mining problems through a new constraint. Secondly, we propose a new filtering algorithm to handle the frequency and closeness constraints. Experiments performed on standard benchmark datasets with numerous mining constraints show that our approach enables to find more informative quantitative patterns, which are better in running time than quantitative approaches based on classical Boolean patterns.
{"title":"A constraint programming approach for quantitative frequent pattern mining","authors":"Mohammed El Amine Laghzaoui, Yahia Lebbah","doi":"10.1504/ijdmmm.2023.132973","DOIUrl":"https://doi.org/10.1504/ijdmmm.2023.132973","url":null,"abstract":"Itemset mining is the first pattern mining problem studied in the literature. Most of the itemset mining studies have considered only Boolean datasets, where each transaction can contain or not items. In practical applications, items appear in some transactions with some quantities. In this paper, we propose an extension of the current efficient constraint programming approach for itemset mining, to take into account quantitative items in order to find patterns with their quantities directly on the original quantitative dataset. The contribution is two folds. Firstly, we facilitate the modelling task of mining problems through a new constraint. Secondly, we propose a new filtering algorithm to handle the frequency and closeness constraints. Experiments performed on standard benchmark datasets with numerous mining constraints show that our approach enables to find more informative quantitative patterns, which are better in running time than quantitative approaches based on classical Boolean patterns.","PeriodicalId":43061,"journal":{"name":"International Journal of Data Mining Modelling and Management","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136256976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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