Data Mining Classification Algorithms Research Articles

Retraction: A review on bioinformatics using data mining techniques (J. Phys.: Conf. Ser. 1228 012023)

This article has been retracted by IOP Publishing following an allegation that this article may contain tortured phrases [1]. IOP Publishing has investigated in line with the COPE guidelines and have found tortured phrases to be masking overlap of other work [2-5]. IOP Publishing has invited the authors to engage in a discussion regarding these issues but had no response to date. Due to the issues identified and the lack of response from the authors, the journal has lost confidence in the validity of the findings presented and agree this article should be retracted. IOP Publishing wishes to credit PubPeer commenters [6] and the Problematic Paper Screener for bringing the issue to our attention. [1] Cabanac G, Labbe C, Magazinov A, 2021, arXiv:2107.06751v1 [2] A. M. Mabu, R. Prasad, R. Yadav and S. S. Jauro, 2018, A Review of Data Mining Methods in Bioinformatics, Recent Advances on Engineering, Technology and Computational Sciences (RAETCS) pp. 1-6 [3] M.A Masood and M.N.A Khan, 2015, Clustering Techniques in Bioinformatics. I.J. Modern Education and Computer Science. 1 38-46 [4] C. Narasimha Chary, A Krishna, N Abhishek, and R.P Singh, 2018, An Efficient Survey on various Data Mining Classification Algorithms in Bioinformatics, International Journal of Engineering and Techniques 4 [5] S. Muthulakshmi and R. Porkodi, 2016, A Survey on Classification Algorithms in Data Mining of Bioinformatics, IJSRD 4 3 [6] https://pubpeer.com/publications/279D6D4AC8BBB9F3263B99279239C3 Retraction published: 31 August 2022

A Comparative Study on Traditional Data Mining and Big Data Mining Classification Algorithms

A Comparative Study on Traditional Data Mining and Big Data Mining Classification Algorithms

S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

Data mining has proven to be very useful in order to extract information from data in many different contexts. However, due to the complexity of data mining techniques, it is required the know-how of an expert in this field to select and use them. Actually, adequately applying data mining is out of the reach of novice users which have expertise in their area of work, but lack skills to employ these techniques. In this paper, we use both model-driven engineering and scientific workflow standards and tools in order to develop named S3Mining framework, which supports novice users in the process of selecting the data mining classification algorithm that better fits with their data and goal. To this aim, this selection process uses the past experiences of expert data miners with the application of classification techniques over their own datasets. The contributions of our S3Mining framework are as follows: (i) an approach to create a knowledge base which stores the past experiences of experts users, (ii) a process that provides the expert users with utilities for the construction of classifiers’ recommenders based on the existing knowledge base, (iii) a system that allows novice data miners to use these recommenders for discovering the classifiers that better fit for solving their problem at hand, and (iv) a public implementation of the framework’s workflows. Finally, an experimental evaluation has been conducted to shown the feasibility of our framework.

PERFORMANCE EVALUATION OF CLASSIFICATION ALGORITHMS IN DATA MINING AND AN APPLICATION WITH THE R LANGUAGE

Sınıflandırma Yöntemi, veri madenciliğinin başlıca yöntemlerinden biri olup, öğrenme algoritmasına dayanır. Büyük ölçekli bir veri içinde gizli kalmış bir örüntüyü keşfetmek amacıyla uygulanır. Veri madenciliği kapsamında, örüntü, bir varlık için dijital ortamda kaydedilmiş; gözlemlenebilir, ölçülebilir ve tekrar edilebilir bir bilgi olarak ifade edilmektedir. Ulaşılmak istenen bilginin elde edilmesi için uygulanan sınıflandırma algoritmaları, içerdiği verinin ortak özelliğine göre veri setinin belirli sınıflara ayrılmasını (ayrıklaştırılmasını) sağlamaktadırlar. Bu işlemin ardından bir sınıflandırma modeli elde edilir. Elde edilen sınıflandırma modeli yeni bir veri seti üzerinde uygulanarak, model ile belirlenmiş olan sınıfların veri seti içindeki benzerlerinin varlığı araştırılır. Söz konusu işlem “örüntü tanıma” olarak isimlendirilmektedir. Bu çalışmada, veri madenciliğinde sınıflandırma süreci ele alınarak, C5.0 ve Gini isimli iki farklı sınıflandırma algoritması ile bir uygulama gerçekleştirilmiştir. Bu amaçla açık kaynak kodlu R dili uygulanarak, her iki sınıflandırma modelinin tahmin değerlerinin doğruluğuyla ilgili performans ölçüm değerleri elde edilmiştir. Ayrıca, en iyi performans ölçüm değerine sahip bir model ele alınarak, sonuçları değerlendirilmiştir

Comparison of Data Mining Classification Algorithms Determining the Default Risk

Big data and its analysis have become a widespread practice in recent times, applicable to multiple industries. Data mining is a technique that is based on statistical applications. This method extracts previously undetermined data items from large quantities of data. The banking and insurance industries use data mining analysis to detect fraud, offer the appropriate credit or insurance solutions to customers, and better understand customer demands. This study aims to identify data mining classification algorithms and use them to predict default risks, avoid possible payment difficulties, and reduce potential problems in extending credit. The data for this study, which contains demographic and socioeconomic characteristics of individuals, were obtained from the Turkish Statistical Institute 2015 survey. Six classification algorithms—Naive Bayes, Bayesian networks, J48, random forest, multilayer perceptron, and logistic regression—were applied to the dataset using WEKA 3.9 data mining software. These algorithms were compared considering the root mean error squares, receiver operating characteristic area, accuracy, precision, F-measure, and recall statistical criteria. The best algorithm—logistic regression—was obtained and applied to the real dataset to determine the attributes causing the default risk by using odds ratios. The socioeconomic and demographic characteristics of the individuals were examined, and based on the odds ratio values, the results of which individuals and characteristics were more likely to default, were reached. These results are not only beneficial to the literature but also have a significant influence in the financial industry in terms of the ability to predict customers’ default risk.

Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study

Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study

Optimizing Partition Granularity, Membership Function Parameters, and Rule Bases of Fuzzy Classifiers for Big Data by a Multi-objective Evolutionary Approach

Classical data mining algorithms are considered inadequate to manage the volume, variety, velocity, and veracity aspects of big data. The advent of a number of open-source cluster-computing frameworks has opened new interesting perspectives for handling the volume and velocity features. In this context, thanks to their capability of coping with vague and imprecise information, distributed fuzzy models appear to be particularly suitable for handling the variety and veracity features of big data. Moreover, the interpretability of fuzzy models may assume a particular relevance in the context of big data mining. In this work, we propose a novel approach for generating, out of big data, a set of fuzzy rule–based classifiers characterized by different optimal trade-offs between accuracy and interpretability. We extend a state-of-the-art distributed multi-objective evolutionary learning scheme, implemented under the Apache Spark environment. In particular, we exploit a recently proposed distributed fuzzy decision tree learning approach for generating an initial rule base that serves as input to the evolutionary process. Furthermore, we integrate the evolutionary learning scheme with an ad hoc strategy for the granularity learning of the fuzzy partitions, along with the optimization of both the rule base and the fuzzy set parameters. Experimental investigations show that the proposed approach is able to generate fuzzy rule–based classifiers that are significantly less complex than the ones generated by the original multi-objective evolutionary learning scheme, while keeping the same accuracy levels.

Data Mining Techniques for Predicting Employability in Morocco

One of the biggest challenges for Big Data applications is to explore large volumes of data and extract valuable information and knowledge for future actions. Employment is the main form of social integration, a factor in improving living conditions and preventing risks of poverty and vulnerability and the most appropriate indicator for assessing the level of social cohesion in a country. Mining employability data will give decision makers a great view of the data and opportunities to make improvement in this sector. In this paper, we presented an experimental study comparing various classification data mining algorithms on employability data in Morocco, which are Decision tree, Logistic regression and Naïve Bayes, which take place in the top 10 data mining algorithms identified by the IEEE International Conference on Data mining. The objective in our experiment is to choose the most efficient and suited algorithm for the employability data. Â

Data Mining Driven Models for Diagnosis of Diabetes Mellitus: A Survey

Objective: In this study, a systematic effort was employed to identify and review data mining concept, tasks and model evaluation techniques, Knowledge Discovery and Data mining process Model (KDDM) model process and research articles published with reputable journal publishers that employed data mining techniques for diagnosis of Diabetes Mellitus. Method/Analysis: The findings from this work have been drawn from the published articles reviewed and the frequency analysis was used for the analysis of the reviewed works. Finding: The result of the study showed that, classification data mining task has been the most successfully and most frequently used data mining tasks for diagnosis of DM and the mostly commonly used classification data mining algorithms are Support Vector Machine and decision tree algorithms. Novelty/Improvement: In the study Support Vector Machine was realized to be most efficient data mining algorithm for diagnosis of Diabetes Mellitus using either clinical or biological and clinical dataset of Diabetes Mellitus. Despite its popularity, SVM algorithm should be further improved in the future work so as to further improve its efficiency. Keywords: Algorithm, Data Mining, Diabetes; Diagnosis, Knowledge, Pattern

Research on bank credit default prediction based on data mining algorithm

It is of great importance to identify the potential risks to the bank's loan customers. Based on data mining technology, it is an effective method to classify loan customers by classification algorithm. In this paper, we use Random Forest method, Logistic Regression method, SVM method and other suitable classification algorithms by python to study and analyze the bank credit data set, and compared these models on five model effect evaluation statistics of Accuracy, Recall, precision, F1-score and ROC area. This paper use the data mining classification algorithm to identify the risk customers from a large number of customers to provide an effective basis for the bank's loan approval.

EF_Unique: An Improved Version of Unsupervised Equal Frequency Discretization Method

Discretization is an important data preprocessing technique used in data mining and knowledge discovery processes. The purpose of discretization is to transform or partition continuous values into discrete ones. In this manner, many data mining classification algorithms can be applied the discrete data more concisely and meaningfully than continuous ones, resulting in better performance. In this study, an improved version of the unsupervised equal frequency (EF) discretization method, EF_Unique, is proposed for enhancing the performance of discretization. The proposed EF_Unique discretization method is based on the unique values of the attribute to be discretized. In order to test the success of the proposed method, 17 benchmark datasets from the UCI repository and four data mining classification algorithms were used, namely Naive Bayes, C.45, k-nearest neighbor, and support vector machine. The experimental results of the proposed EF_Unique discretization method were compared with those obtained using well-known discretization methods; unsupervised equal width (EW), EF, and supervised entropy-based ID3 (EB-ID3). The results show that the proposed EF_Unique discretization method outperformed EW, EF, and EB-ID3 discretization methods in 43, 41, and 27 out of the 68 benchmark tests, respectively.

A classification framework for drug relapse prediction

This paper proposes a framework for relapse prediction using Artificial Neural Network algorithms among drug addicts at Pusat Rawatan Inabah. The data collected will be mining through Artificial Neural Network algorithms to generate patterns and useful knowledge and then automatically classifying the relapse possibility. This research collaborates with Pusat Rawatan Inabah, which is one of the rehabilitation centers that provide a specific treatment to rehabilitate the drug addicts from addiction. We expect that among the classification data mining algorithms, Artificial Intelligence Neural Network (ANN) is one of the best algorithms to predict relapse among drug addicts. This may help the rehabilitation center to predict relapse individually and the prediction result is hoped to prevent drug addicts from relapse. Keywords : classification; artificial neural network; drug addiction; Inabah rehabilitation.

Spatial big data for disaster management

Big data is an idea of informational collections that depicts huge measure of information and complex that conventional information preparing application program is lacking to manage them. Presently, big data is a widely known domain used in research, academic, and industries. It is utilized to store substantial measure of information in a solitary brought together one. Challenges integrate capture, allocation, analysis, information precise, visualization, distribution, interchange, delegation, inquiring, updating and information protection. In this digital world, to put away the information and recovering the data is enormous errand for the huge organizations and some time information ought to be misfortune due to circulated information putting away. For this issue the organization individuals are chosen to actualize the huge information to put away every one of the information identified with the organization they are put away in one enormous database that is known as large information. Remote sensor is a science getting data used to distinguish the items or break down the range from a separation. It is anything but difficult to discover the question effortlessly with the sensor. It makes geographic data from satellite and sensor information so in this paper dissect what are the structures are utilized for remote sensor in huge information and how the engineering is vary from each other and how they are identify with our investigations. This paper depicts how the calamity happens and figuring consequence of informational collection. And applied a seismic informational collection to compute the tremor calamity in view of classification and clustering strategy. The classical data mining algorithms for classification used are k-nearest, naive bayes and decision table and clustering used are hierarchical, make density based and simple k_means using XLMINER and WEKA tool. This paper also helps to predicts the spatial dataset by applying the XLMINER AND WEKA tool and thus the big spatial data can be well suited to this paper.

Diagnosis of Mesothelioma Disease Using Different Classification Techniques

Mesothelioma, which is a disease of the pleura and peritoneum, is an asbestos-related environmental disease in undeveloped countries. Although the incidence of this disease is lower than that of lung cancer, the reaction it creates in society is very high. In this study, 9 different classification algorithms of data mining were applied to the Mesethelioma data set obtained from real patients in Dicle University, Faculty of Medicine and loaded into UCI Machine Learning Repository, and the results were compared. When the obtained results were examined, it has been seen that Artificial Neural Network (ANN) had %99.0740 correct classification ratio.

ACPRISM: Associative classification based on PRISM algorithm

Associative classification (AC) is an integration between association rules and classification tasks that aim to predict unseen samples. Several studies indicate that the AC algorithms produce more accurate results than classical data mining algorithms. However, current AC algorithms inherit from association rules two major drawbacks resulting in a massive set of generated rules, in addition to a very large number of models (classifiers). In response to these two drawbacks, a new AC algorithm based on PRISM algorithm (ACPRISM) is proposed which employs the power of the PRISM algorithm to decrease the number of generated rules.To investigate the efficiency and the performance of the proposed algorithm, five different algorithms were tested, namely FACA, CBA, MAC, PRISM and RIPPER. Two experiments were conducted on groundwater and 16 different well-known datasets using predictive accuracy (%), number of generated rules and time taken to build the model (learning times).Our experimental results show that the ACPRISM produced the lowest number of rules, and is much more efficient and more scalable than all considered algorithms with regard to learning times. Finally, the ACPRISM outperformed the CBA, MCAR, PRISM and RIPPER algorithms in terms of predictive accuracy, and produced comparable results to the FACA algorithm.

Prediction of leptospirosis cases using classification algorithms

Leptospirosis is a potentially life-threatening disease primarily affecting low-income populations, with an estimated annual incidence of 1.03 million infections worldwide. This disease has symptoms often confused with other febrile syndromes, such as dengue fever, influenza and viral hepatitis, often making diagnosis challenging. Improving the accuracy of early diagnosis of patients with leptospirosis will increase the speed of appropriate antibiotic treatment delivery, and both will improve clinical outcomes for this potentially fatal disease. The authors conducted an analysis of clinically and epidemiologically defined leptospirosis cases to predict disease using data mining classification algorithms. They conducted four sets of experiments to evaluate the performance of the algorithms, assessing their predictive accuracy of using different training and test datasets. The JRIP algorithm achieved 84% sensitivity using a dataset of only confirmed leptospirosis cases, and a specificity of 99% using a dataset of only confirmed dengue cases. Therefore, the approach successfully predicted leptospirosis cases, differentiated them from similar febrile illnesses, and may represent a new tool to assist health professionals, particularly in endemic areas for leptospirosis, accelerating targeted treatment and minimising disease exacerbation and mortality.

Student Academic Performance Prediction on Problem Based Learning Using Support Vector Machine and K-Nearest Neighbor

Academic evaluation is an important process to know how well the learning process was conducted and also one of the decisive factors that can determine the quality of the higher education institution. Though it usually curative, the preventive effort is needed by predicting the performance of the student before the semester begin. This effort aimed to reduce the failure rate of the students in certain subjects and make it easier for the PBL tutor to create appropiate learning strategies before the tutorial class begin. The purpose of this work is to find the best data mining technique to predict student academic performance on PBL system between two data mining classification algorithms. This work applied and compared the performance of the classifier models built from Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). After preprocessed the dataset, the classifier models were developed and validated. The result shows that both algorithms were giving good accuracy by 97% and 95,52% respectively though SVM showing the best performance compared to KNN in F-Measure with 80%. The further deployment is needed to integrate the model with academic information system, so that academic evaluation can be easily done.

English

English

Automatic Classification on Bio Medical Prognosisof Invasive Breast Cancer

Breast Cancer one of the appalling diseases among the middle-aged women and it is a foremost threatening death possibility cancer in women throughout the world. Earlier prognosis and preclusion reduces the conceivability of death. The proposed system beseech various data mining techniques together with a real-time input data from a biosensor device to determine the disease development proportion. Surface acoustic waves (SAW) biosensor empowers a label-free, worthwhile and straight detection of HER-2/neu cancer biomarker. The output from the biosensor is fed into the proposed system as an input along with data collected from Winconsin dataset. The complete dataset are processed using data mining classification algorithms to predict the accuracy. The exactness of the proposed model is improved by ranking attributes by Ranker algorithm. The results of the proposed model are highly gifted with an accuracy of 79.25% with SVM classifier and an ROC area of 0.754 which is better than other existing systems. The results are used in designing the proper drug thereby improving the survivability of the patients.

COMPARISON OF CLASSIFICATION ALGORITHMS TO DETECT PHISHING WEB PAGES USING FEATURE SELECTION AND EXTRACTION

The phishing is a kind of e-commerce lure which try to steal the confidential information of the web user by making identical website of legitimate one in which the contents and images almost remains similar to the legitimate website with small changes. Another way of phishing is to make minor changes in the URL or in the domain of the legitimate website. In this paper, a number of anti-phishing toolbars have been discussed and proposed a system model to tackle the phishing attack. The proposed anti-phishing system is based on the development of the Plug-in tool for the web browser. The performance of the proposed system is studied with three different data mining classification algorithms which are Random Forest, Nearest Neighbour Classification (NNC), Bayesian Classifier (BC). To evaluate the proposed anti-phishing system for the detection of phishing websites, 7690 legitimate websites and 2280 phishing websites have been collected from authorised sources like APWG database and PhishTank. After analyzing the data mining algorithms over phishing web pages, it is found that the Bayesian algorithm gives fast response and gives more accurate results than other algorithms.