Data Mining Classification Algorithms Research Articles

Predicting Performance of Schools by Applying Data Mining Techniques on Public Examination Results

This study work presents a systematic analysis of various features of the higher grade school public examination results data in the state of Tamil Nadu, India through different data mining classification algorithms to predict the performance of Schools. Nowadays the parents always targets to select the right city, school and factors which contributes to the success of the results in schools of their children. There could be possible effects of factors such as Ethnic mix, Medium of study, geography could make a difference in results. The proposed work would focus on two fold factors namely Machine Learning algorithms to predict School performance with satisfying accuracy and to evaluate the data mining technique which would give better accuracy of the learning algorithms. It was found that there exist some apparent and some less noticeable attributes that demonstrate a strong correlation with student performance. Data were collected through the credible source data preparation and correlation analysis. The findings revealed that the public examinations results data was a very helpful predictor of performance of school in order to improve the result with maximum level and also improved the overall accuracy with the help of Adaboost technique.

A Novel and Advanced Data Mining Model based Hybrid Intrusion Detection Framework

An Intrusion can be defined as any practice or act that attempt to crack the integrity, confidentiality or availability of a resource. This may contain of a deliberate unauthorized attempt to access the information, manipulate the data, or make a system unreliable or unusable. With the expansion of computer networks at an alarming rate during the past decade, security has become one of the serious issues of computer systems. IDS, is a detection mechanism for detecting the intrusive activities hidden among the normal activities. The revolutionary establishment of IDS has attracted analysts to work dedicatedly enabling the system to deal with technological advancements. Hence, in this regard, various beneficial schemes and models have been proposed in order to achieve enhanced IDS. This paper proposes a novel hybrid model for intrusion detection. The proposed framework in this paper may be expected as another step towards advancement of IDS. The framework utilizes the crucial data mining classification algorithms beneficial for intrusion detection. The Hybrid framework would henceforth, will lead to effective, adaptive and intelligent intrusion detection. DOI: http://dx.doi.org/10.11591/telkomnika.v13i2.7045

Accuracies and Training Times of Data Mining Classification Algorithms: An Empirical Comparative Study

Two important performance indicators for data mining algorithms are accuracy of classification/ prediction and time taken for training. These indicators are useful for selecting best algorithms for classification/prediction tasks in data mining. Empirical studies on these performance indicators in data mining are few. Therefore, this study was designed to determine how data mining classification algorithm perform with increase in input data sizes. Three data mining classification algorithms—Decision Tree, Multi-Layer Perceptron (MLP) Neural Network and Naive Bayes— were subjected to varying simulated data sizes. The time taken by the algorithms for trainings and accuracies of their classifications were analyzed for the different data sizes. Results show that Naive Bayes takes least time to train data but with least accuracy as compared to MLP and Decision Tree algorithms.

Computer Vision-Based Real-Time 3D Gesture Recognition Using Depth Image

Gesture is one of the fundamental ways of human machine natural interaction. To understand gesture, the system should be able to interpret 3D movements of human. This paper presents a computer vision-based real-time 3D gesture recognition system using depth image which tracks 3D joint position of head, neck, shoulder, arms, hands and legs. This tracking is done by Kinect motion sensor with OpenNI API and 3D motion gesture is recognized using the movement trajectory of those joints. User to Kinect sensor distance is adapted using proposed center of gravity (COG) correction method and 3D joint position is normalized using proposed joint position normalization method. For gesture learning and recognition, data mining classification algorithms such as Naive Bayes and neural network is used. The system is trained to recognize 12 gestures used by umpires in a cricket match. It is trained and tested using about 2000 training instances for 12 gesture of 15 persons. The system is tested using 5-fold cross validation method and achieved 98.11% accuracy with neural network and 88.84% accuracy with Naive Bayes classification method.

Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events.

Ciprofloxacin is one of the main drugs to treat bacterial infections. Bacterial infections can lead to high morbidity, mortality, and costs of treatment in the world. In this study, an analysis was conducted using the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) database on the adverse events of ciprofloxacin. The aim of this study was to explore unknown associations among the adverse events of ciprofloxacin, patient demographics and adverse event outcomes. A search of FDA AERS reports was performed and some statistics was highlighted. The most frequent adverse events and event outcomes of ciprofloxacin were listed, age and gender specific distribution of adverse events are reported, then the apriori algorithm was applied to the dataset to obtain some association rules and objective measures were used to select interesting ones. Furthermore, the results were compared against classical data mining algorithms and discussed. The search resulted in 6 531 reports. The reports included within the dataset consist of 3 585 (55.8%) female and 2 884 (44.1%) male patients. The mean age of patients is 54.59 years. Preschool child, middle aged and aged groups have most adverse events reports in all groups. Pyrexia has the highest frequency with ciprofloxacin, followed by pain, diarrhoea, and anxiety in this order and the most frequent adverse event outcome is hospitalization. Age and gender based differences in the events in patients were found. In addition, some of the interesting associations obtained from the Apriori algorithm include not only psychiatric disorders but specifically their manifestation in specific gender groups. The FDA AERS offers an important data resource to identify new or unknown adverse events of drugs in the biomedical domain. The results that were obtained in this study can provide valuable information for medical researchers and decision makers at the pharmaceutical research field.

Comparative Analysis of Data Mining Classification Algorithms in Type-2 Diabetes Prediction Data Using WEKA Approach

The goal of this paper discusses about different types of data mining classification algorithms accuracies that are widely used to extract significant knowledge from huge amounts of data. Here illustrate 20 classifications of supervised data mining algorithms base on type-2 diabetes disease dataset perspective to Bangladeshi populations. In this paper we compare 20 classification algorithms by measuring accuracies, speed and robustness of those algorithms using WEKA toolkit version 3.6.5. Accuracies of classification algorithms are measured in 3 cases like Total Training data set, 10 fold Cross Validation and Percentage Split (66% taken). Speed (CPU Execution Time) and error rate also measured as like as accuracy. Firstly checked top perform algorithms that have best outcome for different cases and then ranked top outcomes algorithms. Finally ranked best 5 algorithms among 20 algorithms based on their accuracies.

Multi-label rules for phishing classification

Generating multi-label rules in associative classification (AC) from single label data sets is considered a challenging task making the number of existing algorithms for this task rare. Current AC algorithms produce only the largest frequency class connected with a rule in the training data set and discard all other classes even though these classes have data representation with the rule’s body. In this paper, we deal with the above problem by proposing an AC algorithm called Enhanced Multi-label Classifiers based Associative Classification (eMCAC). This algorithm discovers rules associated with a set of classes from single label data that other current AC algorithms are unable to induce. Furthermore, eMCAC minimises the number of extracted rules using a classifier building method. The proposed algorithm has been tested on a real world application data set related to website phishing and the results reveal that eMCAC’s accuracy is highly competitive if contrasted with other known AC and classic classification algorithms in data mining. Lastly, the experimental results show that our algorithm is able to derive new rules from the phishing data sets that end-users can exploit in decision making.

DETERMINING AFFECTING FACTORS OF HYPERTENSION WITH DATA MINING TECHNIQUES

In this paper, Age, Gender, Body Mass Index, HDL, LDL, Triglyceride, Uric Acid and The Use of Smoking data gathered from 150 patients are analyzed with data mining classification algorithms. The data is divided into two different classes which are normal and patient. Thus, a diagnostic system is developed which predicts whether a candidate patient has hypertension or not. Besides, a decision tree is created and factors affecting hypertension directly and indirectly are determined. In this study, C4.5, Naive Bayes and Multilayer perceptron classification algorithms are used, and shown that C4.5 algorithm gives better results.

Predicting construction cost overruns using text mining, numerical data and ensemble classifiers

Abstract This paper discusses how text describing a construction project can be combined with numerical data to produce a prediction of the level of cost overrun using data mining classification algorithms. Modeling results found that a stacking model that combined the results from several classifiers produced the best results. The stacking ensemble model had an average accuracy of 43.72% for five model runs. The model performed best in predicting projects completed with large cost overruns and projects near the original low bid amount. It was found that a stacking model that used only numerical data produced predictions with lower precision and recall. A potential application of this research is as an aid in budgeting sufficient funds to complete a construction project. Additionally, during the planning stages of a project the research can be used to identify a project that requires increased scrutiny during construction to avoid cost overruns.

A Predictive Model for Assistive Technology Adoption for People With Dementia

Assistive technology has the potential to enhance the level of independence of people with dementia, thereby increasing the possibility of supporting home-based care. In general, people with dementia are reluctant to change; therefore, it is important that suitable assistive technologies are selected for them. Consequently, the development of predictive models that are able to determine a person's potential to adopt a particular technology is desirable. In this paper, a predictive adoption model for a mobile phone-based video streaming system, developed for people with dementia, is presented. Taking into consideration characteristics related to a person's ability, living arrangements, and preferences, this paper discusses the development of predictive models, which were based on a number of carefully selected data mining algorithms for classification. For each, the learning on different relevant features for technology adoption has been tested, in conjunction with handling the imbalance of available data for output classes. Given our focus on providing predictive tools that could be used and interpreted by healthcare professionals, models with ease-of-use, intuitive understanding, and clear decision making processes are preferred. Predictive models have, therefore, been evaluated on a multi-criterion basis: in terms of their prediction performance, robustness, bias with regard to two types of errors and usability. Overall, the model derived from incorporating a k-Nearest-Neighbour algorithm using seven features was found to be the optimal classifier of assistive technology adoption for people with dementia (prediction accuracy 0.84 ± 0.0242).

A Feature Subset Selection Technique for High Dimensional Data Using Symmetric Uncertainty

With the abundance of exceptionally High Dimensional data, feature selection has become an essential element in the Data Mining process. In this paper, we investigate the problem of efficient feature selection for classification on High Dimensional datasets. We present a novel filter based approach for feature selection that sorts out the features based on a score and then we measure the performance of four different Data Mining classification algorithms on the resulting data. In the proposed approach, we partition the sorted feature and search the important feature in forward manner as well as in reversed manner, while starting from first and last feature simultaneously in the sorted list. The proposed approach is highly scalable and effective as it parallelizes over both attribute and tuples simultaneously allowing us to evaluate many of potential features for High Dimensional datasets. The newly proposed framework for feature selection is experimentally shown to be very valuable with real and synthetic High Dimensional datasets which improve the precision of selected features. We have also tested it to measure classification accuracy against various feature selection process.

An Analysis of students’ performance using classification algorithms

In recent years, the analysis and evaluation of students performance and retaining the standard of education is a very important problem in all the educational institutions. The most important goal of the paper is to analyze and evaluate the school students performance by applying data mining classification algorithms in weka tool. The data mining tool has been generally accepted as a decision making tool to facilitate better resource utilization in terms of students performance. The various classification algorithms could be specifically mentioned as J48, Random Forest, Multilayer Perceptron, IB1 and Decision Table are used. The results of such classification model deals with accuracy level, confusion matrices and also the execution time. Therefore conclusion could be reached that the Random Forest performance is better than that of different algorithms. I. Introduction Data Mining could be a promising and flourishing frontier in analysis of data and additionally the result of analysis has many applications. Data Mining can also be referred as Knowledge Discovery from Data (KDD).This system functions as the machine-driven or convenient extraction of patterns representing knowledge implicitly keep or captured in huge databases, data warehouses, the Web, data repositories, and information streams. Data Mining is a multidisciplinary field, encompassing areas like information technology, machine learning, statistics, pattern recognition, data retrieval, neural networks, information based systems, artificial intelligence and data visualization. The application of data mining is widely prevalent in education system. Educational data mining is an emerging field which can be effectively applied in the field of education. The educational data mining uses several ideas and concepts such as Association rule mining, classification and clustering. The knowledge that emerges can be used to better understand students' promotion rate, students' retention rate, students' transition rate and the students' success. The data mining system is pivotal and crucial to measure the students' performance improvement. The classification algorithms can be used to classify and analyze the students' data set in accurate manner. The students' academic performance is influenced by various factors like parents' education, locality, economic status, attendance, gender and result. The main objective of the paper is to use data mining methodologies to study and analyze the school students' performance. Data mining provides many tasks that could be used to study the students' performance. In this paper, the classification task is employed to gauge students' performance and deals with the accuracy, confusion matrices and the execution time taken by the various classification data mining algorithms. This paper is catalogued as follows. Section 2 enumerates a related work. Section 3 presents the idea of Classification and discusses the aspects of classification algorithm. Section 4 elaborates a Data Preprocessing. Section 5 explains the Implementation of model construction. Section 6 describes the results and discussions. Section 7 provides the conclusion. II. Related Work Alaael-Halees 2009 suggested that Data Mining is an emerging methodology used in educational field to enhance the understanding of learning process. The application of Data mining is widely spread in Higher Education system. In Education domain many researchers and authors have explored and discussed various applications of data mining in higher education. The authors had gone through the survey of the literature to understand the importance of data mining applications. In the year 2001 Luan al. suggested a powerful decision support tool called data mining. Data Mining is a powerful tool for academic purposes Alumni, Institutional effectiveness, marketing and enrollment can benefit from the use of data mining Data Mining is the most suited technology that can be used by lecturer, student, alumnus, manager and other educational staff and is a useful tool for decision making on their educational activities

An urgent precaution system to detect students at risk of substance abuse through classification algorithms

In recent years, the use of addictive drugs and substances has turned out to be a challenging social problem worldwide. The illicit use of these types of drugs and substances appears to be increasing among elementary and high school students. After becoming addicted to drugs, life becomes unbearable and gets even worse for their users. Scientific studies show that it becomes extremely difficult for an individual to break this habit after being a user. Hence, preventing teenagers from addiction becomes an important issue. This study focuses on an urgent precaution system that helps families and educators prevent teenagers from developing this type of addiction. The aim of this study is to detect a teenager's probability of being a drug abuser using classification algorithms in machine learning and data mining. The objective is not to test the classifiers theoretically on the benchmark datasets, but rather to use this study as a basis for advanced and detailed studies in this field in the future. This paper not only uses a special dataset but also focuses on psychometrics and statistics. The findings of this study show that if there is a computed high risk for a teenager, some precautions, if necessary, may be taken by educators and parents to keep the teenager away from those substances.

A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms

A Proposal to Detect Computer Worms (Malicious Codes) Using Data Mining Classification Algorithms

A Kind of Classification Algorithms of Data Mining and Quantitative Analysis

C4.5, Bayesian network and Sequential Minimal Optimization (SMO) are three typical classification algorithms in data mining. Using cross-validation method with 10 folds get analysis and calculation results of the experiments for the three classification algorithms in the same training set and test set. The main metrics include accuracy, precision, speed, robustness, scalability and comprehensibility, we use margin curve show these. It provides a theoretical and experimental basis for users to select a proper classification algorithm with different training sets in quality and amount.

Performance Analysis and Evaluation of Different Data Mining Algorithms used for Cancer Classification

Classification algorithms of data mining have been successfully applied in the recent years to predict cancer based on the gene expression data. Micro-array is a powerful diagnostic tool that can generate handful information of gene expression of all the human genes in a cell at once. Various classification algorithms can be applied on such micro-array data to devise methods that can predict the occurrence of tumor. However, the accuracy of such methods differ according to the classification algorithm used. Identifying the best classification algorithm among all available is a challenging task. In this study, we have made a comprehensive comparative analysis of 14 different classification algorithms and their performance has been evaluated by using 3 different cancer data sets. The results indicate that none of the classifiers outperformed all others in terms of the accuracy when applied on all the 3 data sets. Most of the algorithms performed better as the size of the data set is increased. We recommend the users not to stick to a particular classification method and should evaluate different classification algorithms and select the better algorithm.

Efficient Disease Classifier Using Data Mining Techniques: Refinement of Random Forest Termination Criteria

In biomedical field, the classification of disease using data mining is the critical task. The prediction accuracy plays a vital role in disease data set. More data mining classification algorithms like decision trees, neural networks, Bayesian classifiers are used to diagnosis the diseases. In decision tree Random Forest, Initially a forest is constructed from ten tress. The accuracy is measured and compared with desired accuracy. If the selected best split of trees matched the desired accuracy the construction terminates. Otherwise a new tree is added with random forest and accuracy is measured. The fitting criteria of random forest are accuracy and correlation. The accuracy is based on the mean absolute percentage error (MAPE) and the mean absolute relative error (MARE).In proposed system to refine the termination criteria of Random Forest, Binomial distribution, multinomial distribution and sequential probability ratio test (SPRT) are used. The proposed method stops the random forest earlier compared with existing Random Forest algorithm. The supervised learning model like support vector machine takes a set of inputs and analyze the inputs and recognize the desired patterns. The disease data sets are supplied to SVM and prediction accuracy is measured. The comparison is made between Random Forest and SVM and best class labels are identified based on disease.

Performance Evaluation of Different Data Mining Classification Algorithm and Predictive Analysis

Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information; data mining has become an essential component in various fields of human life. It is used to identify hidden patterns in a large data set. Classification techniques are supervised learning techniques that classify data item into predefined class label. It is one of the most useful techniques in data mining to build classification models from an input data set; these techniques commonly build models that are used to predict future data trends. In this paper we have worked with different data mining applications and various classification algorithms, these algorithms have been applied on different dataset to find out the efficiency of the algorithm and improve the performance by applying data preprocessing techniques and feature selection and also prediction of new class labels.

Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction

Data mining is an iterative development within which evolution is defined by discovery, through either usual or manual methods. In this paper using the data mining concept to CDMCA classifies two types supervised and unsupervised classifications. Here illustrate the classification of supervised data mining algorithms base on diabetes disease dataset. It encompass the diseases plasma glucose at least mentioned value. The research describes algorithmic discussion of C4.5, SVM, K-NN, PNN, BLR, MLR, CRT, CS-CRT, PLS-DA and PLS-LDA. Here used to compare the performance of computing time, precision value and the data evaluated using 10 fold Cross Validation error rate, the error rate focuses True Positive, True Negative, False Positive and False Negative and Accuracy. The outcome CS-CRT algorithm best. The Best results are achieved by using Tanagra tool. Tanagra is data mining matching set. The accuracy is calculate based on addition of true positive and true negative followed by the division of all possibilities.

A comparative analysis of classification algorithms in data mining for accuracy, speed and robustness

Classification algorithms are the most commonly used data mining models that are widely used to extract valuable knowledge from huge amounts of data. The criteria used to evaluate the classifiers a...