A Comparative Study on MMDBM Classifier Incorporating Various Sorting Procedure

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

The way we learn has changed as a result of advances in science and technology and the growing influence of information technology on daily life. E-learning is a relatively new concept in educational settings, but it has progressively earned a spot in modern training techniques. Data mining methods are used in many applications, particularly in elearning. Comparing direct communications difficult to track the student involvement in online courses. To overcome the above mentioned issues the existing methods are analyzed various techniques of data mining and optimization methods which can be applied to bring out hidden knowledge from the educational data? In the e-learning work, preprocessing, feature selection and classification process are performed to improve the Kalboard 360 educational dataset accuracy significantly. This study focused on educational data mining (EDM) and employed decision trees, support vector machines (SVM), multilayer perceptrons, and the naive bayesian C4.5 algorithm among other classification techniques to assess the features for the prediction of students’ behaviour and academic achievement. This paper analyzes the advantages and shortcomings of each algorithm applied to data set on the basis of accuracy for various classification techniques like Multilayer Perceptron based ANN algorithm (MP-ANN), Decision Tree (DT) C4.5 and Hybrid Regression model with Multi Label Classification (HR-MLC) approach. The experimental result shows that the HR-MLC algorithm provides better performance in terms of higher accuracy, precision, recall and f-measure rather than the other existing methods.

The article shows the role of data mining methods at the stages of the e-learning risk management for the various participants. The article proves the e-learning system fundamentally contains heterogeneous information, for its processing it is not enough to use the methods of mathematical analysis but it is necessary to apply the new educational methods of data mining. It determines the basic types of e-learning risks for the elimination or minimization of which proactive manners are applied that aimed at testing and planning other actions. The article gives the rationales for the use of various data mining methods at the different stages of the risk management: 1) for quality authentication of the risks the classification based on using the method called “decision tree” is applied; 2) for making the analysis of risks the brainstorm method for mind map creation is used. The mind map displays the e-learning risk groups that are then rated on the prioritization criteria with the help of expert evaluation method; 3) for the assessment of probability of risk impact on organization of e-learning the expert evaluation method, cluster analysis and bow-tie analysis are used. The article shows that the data mining methods are able to not only classify educational risks but also identify the causes and anticipate possible impacts on the final outcome. Having a great deal of information obtained through the process of the e-learning risk management and using it in data mining, it is possible to determine the reasons and the taken actions depend more the minimization of risks and strengthen the effectiveness of the e-learning.

Churn is a customer's behaviour to quit using product or service and then move to another company. Customer churn is serious problem that must be handled to keep company survive. Handling of churn customers starts from predicting which customers will churn. One approach in predicting customer churn is data mining. This study compares two classification methods in data mining namely artificial neural networks and decision trees in banking industry case. The result showed that the artificial neural network has accuracy 86% and precision 71%, which is better than the decision tree. However, the decision tree has a better recall value than the artificial neural network, which is 58%..

As a result of the huge amounts of data available in any subject of research, data mining technologies confront considerable obstacles. Due to the large volume of data, most existing data mining methods are inapplicable to many real-world problems. Data mining methods become ineffective when the problem size becomes too large. Scalability is an issue that must be addressed in data mining algorithms in order to construct high-performance, efficient, and scalable data mining algorithms. A new scalability technique is proposed and applied to several data mining problems in this research. The proposed scaling methodology is developed using a cascade approach. The approach begins with the collection of a large data set from several sources, which is then preprocessed. Once the dataset has been preprocessed, decompose it into smaller data sets of equal size subsets. Then apply a data mining approach to each subset, with the identical data mining method stated for each subset. The findings of the data mining approach on all subsets are pooled and aggregated for the final output. The performance of the suggested algorithm is assessed using a number of criteria, including accuracy, precision, recall, F-score, and execution time. Social advertising and bank marketing are the two datasets on which the proposed method is tested. The suggested approach's performance is compared with non-scale data mining methods, and it is found that the scaling method outperformed non-scale data mining methods on all measures.

PurposeIn accounting and finance, researchers have used many ways to detect manager’s fraud risk. Until now, many researchers have used some data mining methods in these two fields to detect this risk. The purpose of this paper is to compare the precision of two data mining methods in detecting such a risk.Design/methodology/approachFor this purpose, this paper analyzed the texts of board’s reports and used two methods including the convex optimization (CVX) method and least absolute shrinkage and selection operator (LASSO) regression method. In this way, the words of these reports, which have the greatest power in explaining the manager’s high fraud risk index, were identified. Using these words, this paper presented a model that could detect manager’s high fraud risk index in companies.FindingsThe results indicated that both methods can detect the manager’s high fraud risk index with a precision between 82.55 and 91.25 percent. The LASSO method was significantly more precise than the CVX method.Research limitations/implicationsThe lack of access to an official and reliable list of firms suspected to fraud and the lack of access to the Microsoft Word (MS Word) file of board’s reports were two of the most important limitations of this study.Practical implicationsRegulatory bodies and independent auditors can consider the proposed methods in this study for assessing the fraud risk for a firm or other legal parties.Originality/valueThis paper avoided using merely financial statements data to detect the manager’s fraud risk index and focused on texts of board’s reports for the detection process. The capabilities of data mining and text mining methods for detecting the manager’s fraud risk index using board’s reports were tested in this paper. By comparing CVX and LASSO results, this paper indicated that methods with a binary-dependent variable have more power and are more precise than methods with continuous-dependent variables for detecting fraud.

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledgemind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics [1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.

The rapid development of the digital era is now a major challenge for the education system in Indonesia, especially for high school students. Today's teenagers are in a digital age where the development and enhancement of knowledge in the field of digital technology has had a dramatic impact. Common problems faced by senior high school students include their social relationship, difficulty concentrating in learning, lack of discipline, disobedience to orderliness and disrespect toward teacher and friends. Of these problems have an impact that the student's value is less than the average so that students become class. For that reason, we need a solution to reduce the number of students who failing a grade. Data mining concerns the education world to improve the quality of education by minimizing the number of failing grade students. Classification techniques on data mining used to process data that is Decision Tree and Naïve Bayes. Many data can be managed in schools using the method of data mining, therefore it is necessary to limit the problem so that the assessment can be focused and not floating. Limitations in the problem include Cognitive values, Psychomotor values, Affective values, Number of attendance Number of remedial exams. The results of the analysis will be used to predict the class increase that will affect the quality improvement of students in the future. Measurement results using confusion matrix and ROC curve obtained decision tree accuracy values of 97.5% and naïve bayes of 97.5%.

Computing significant factors quantitatively is an imperative task in understanding the underlying reasons that contribute to a final outcome. In this paper, a case of e-Government ranking is studied by attempting to find the significance of each KPIs which leads to resultant rank of a country. Significant factors in this context are inferred as some degrees of relations between the input variables (which are the KPIs in this case) and the final outcome (the rank). In the past, significant factors were either acquired as first-hand information via direct questioning from users’ satisfaction survey or qualitative inference; typical question is ‘You are satisfied with a particular e-Government service’ by applying a multi-level Likert scale. Respondents answered by choosing one of the following: Strongly agree, Agree, Neutral, Disagree, and Strongly Disagree. The replies are then counted and studied using traditional statistical methods. In this paper, an alternative method by feature selection in data mining is proposed which computes quantitatively the relative importance of each KPI with respective to the predicted class, the rank. The main advantage of feature selection by data mining (FSDM) method is that it considers the cross-dependencies of the variables and how they contribute as a whole predictive model to a particular predicted outcome. In contrast, classical significant factor analysis such as correlogram tells only the strength of correlation between an individual pair of factor and outcome. Another advantage of using data mining method over simple statistic is that the inferred predictive model could be used as a predictor and/or what-if decision simulator; given some values of KPIs a corresponding rank could be guesstimated. A case study of computing significant factors in terms of KPIs that lead to the world rank in from the data of UN e-Government Survey 2010, is presented.

 Abstract—Nowadays, the method of data mining, widely known as KDD (Knowledge-discovery in databases), is researched actively due to increasing amount of data. Decision tree, which is human-readable and understandable, is widely used in the field of rule extraction, data mining, and pattern recognition. Although decision tree is easy for quickly understanding the data, it has many faults in algorithm itself. In this paper, to compensate for these faults, we combined decision table (rough set theory) and ANN (Artificial Neural Network). With this combined algorithm, we could easily obtain more precise rules from iris data. Our experiment was in this order: 1) Find weights by using back propagation (bp) algorithm. (ANN). 2) Use dimensionality reduction method to delete huge amount of irrelevant data. (Rough set theory and decision table). 3) Make decision tree with pruned weights and rules. With ANN algorithm and decision table (dimensionality reduction method), we could easily notice the most influential (high entropy) character of iris data. With this algorithm, we could extract more precise and pruned rule from iris data sets.

Hypertension is a critical public health concern worldwide. Identification of risk factors using traditional multivariable models has been a field of active research. The present study was undertaken to identify risk patterns associated with hypertension incidence using data mining methods in a cohort of Iranian adult population.Data on 6205 participants (44% men) age > 20 years, free from hypertension at baseline with no history of cardiovascular disease, were used to develop a series of prediction models by 3 types of decision tree (DT) algorithms. The performances of all classifiers were evaluated on the testing data set.The Quick Unbiased Efficient Statistical Tree algorithm among men and women and Classification and Regression Tree among the total population had the best performance. The C-statistic and sensitivity for the prediction models were (0.70 and 71%) in men, (0.79 and 71%) in women, and (0.78 and 72%) in total population, respectively. In DT models, systolic blood pressure (SBP), diastolic blood pressure, age, and waist circumference significantly contributed to the risk of incident hypertension in both genders and total population, wrist circumference and 2-h postchallenge plasma glucose among women and fasting plasma glucose among men. In men, the highest hypertension risk was seen in those with SBP > 115 mm Hg and age > 30 years. In women those with SBP > 114 mm Hg and age > 33 years had the highest risk for hypertension. For the total population, higher risk was observed in those with SBP > 114 mm Hg and age > 38 years.Our study emphasizes the utility of DTs for prediction of hypertension and exploring interaction between predictors. DT models used the easily available variables to identify homogeneous subgroups with different risk pattern for the hypertension.

Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. Through illustrating on the basic ideas of decision tree in data mining, in this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm combining ID3 and Association Function(AF) is presented. The experiment results show that the proposed algorithm can overcome ID3's shortcoming effectively and get more reasonable and effective rules

Oil well productivity classification and abundance prediction are important for estimating economic benefit of a well. However, it is difficult to predict because well logs are complex and the amount of data collected today has far exceeded our ability to refine and analyze without the use of automated analysis techniques. In response to the problem above mentioned, data mining technology in recent years has shown the ability for discovering information and effectively extracts information from massive observational data sets that can be used to decisions. Especially, classification and prediction methods, are receiving increasing attention from researchers and practitioners in the domain of petroleum exploration and production (E&P) in China. Therefore, data mining is regarded as one of the ten key techniques for challenging problem of oil exploration and development. In this paper, four distinct kinds of classification and prediction methods in data mining, including decision tree (DT), artificial neural network (ANN), support vector machine (SVM) and Bayesian network are used to two real-world case studies. One is hydrocarbon reservoir productivity classification with 21 samples from 16 wells logging data in Karamay Oilfield 8 th district reservoir. The results show that SVM and Bayesian are superior in the classification accuracy (95.2%) to DT, ANN and SVM, and can be considered as a prominent classification model. Another is reservoir abundance prediction with 17 mature accumulation systems samples in JiYang depression basin. The results show that SVM is superior in the prediction accuracy (91.92%) to DT, ANN and Bayesian, and can be taken as an excellent prediction model.

With the promotion of social information construction and the rapid update and replacement of large capacity storage equipment, the amount of data from every field grows exponentially. Reportedly, the amount of the data accumulated by Shandong Hi-speed Group is very large. These data can satisfy us some daily usefulness, such as query, retrieval, statistics, statements etc. But what is more important is that how can we discover some useful information from the information ocean. This information can be used in real life such as auxiliary decision. This paper is proposed in this historical background. Data mining is a powerful tool for acquiring knowledge from massive data. Some methods of data mining, such as decision tree, support vector machine, Bayesian decision theory, artificial neural network, k-nearest neighbor, association rule mining etc, are commonly used. In this paper, we design a recommendation system of highway ETC card by using the theory of decision tree. The recommendation system can predict whether a car owner is a potential ETC customer or not through the analysis of the vehicle information. Experiments proved that the accuracy rate of the recommendation system is larger than 90%, so it can provide effective information for the extension of China's ETC card.

Strategic decisions in early production planning phases have a high impact on various production aspects. Decision making is often based on vague expert knowledge due to lack of a reliable knowledge base. Implications of this problem are especially observable in the field of assembly planning, which integrates results from various planning disciplines. This paper introduces a new concept and the corresponding data model for application of Data Mining (DM) methods in the field of production assembly planning and product design. The approach contains the usage of existing planning data in order to extrapolate assembly processes. Especially linked product and process data allow the innovative usage of Data Mining methods. The concept presents assistance potentials for development of new products variants along the product emergence process (PEP). With this approach an early cost estimation of assembly processes in series production can be achieved using innovative Data Mining methods. Furthermore, design and planning processes can be supported effectively.