Variety Of Data Mining Techniques Research Articles

Predicting insurance claims through a variety of data mining techniques: facing lots of missing values and moderate class-imbalanced levels

Abstract This paper copes with a real-world classification problem related to the management of claims received in an insurance company. The way to obtain the classifier is not easy due to the high amount of missing values as well as the inherent imbalanced scenario within class labels. Once the data partition has been done, the training set is submitted to an intensive double grid search in order to obtain the most promising type of missing value imputation approach and then a step ahead is done using the best method and it starts the next round of data mining strategies which now falls into data rebalancing umbrella. Again, a grid search from an undersampling and oversampling family with different settings is done taking into account only seen data. The training data obtained after the first grid search are now submitted to the second step according the second grid search in order to get the ready training set for the further classifier training. The main objective of the work is to find the best combination of data mining techniques that suits the data set with a pipeline containing two types of data preparation methods coming from different families. As an outcome, first the problem of the presence of missing values has been addressed and then the data rebalancing techniques has been applied. The study focuses on obtaining classifiers based on Bayesian and lazy approaches as well as decision trees, evaluated on metrics such as the area under the ROC curve (AUC), Cohen’s kappa, Accuracy and the F-measure, among others. The imputation by the mean the mode is preferable to the Expectation Maximization Imputation in the scenario faced in this paper taking into account that the amount of missing values is higher than a forty percent for many features.

Physicochemical reaction mechanism study and model optimization based on big data analysis

The huge amount of data generated by information technology, intelligence and automation has serious redundancy and duplication, which becomes a hidden cost of consuming resources. In the field of physicochemical analysis, for the explanation of mechanism, the analysis of big data can provide reference and integration. The purpose of big data-based analysis is to remove redundancies and inconsistencies in the given raw characterization data to better improve its completeness and accuracy. The paper takes the density matrix of quantum bits, which is different from the classical real number representation, etc. as the representation of feature data, and investigates the cleaning of feature data based on quantum representation from the perspective of the intersection of theoretical physics and computer science. This paper comprehensively describes the basic theories and methods of data mining. On the basis of understanding and analyzing a variety of data mining techniques, it focuses on the BP neural network-based data mining techniques to provide in-depth analysis and elaboration. Further, this paper proposes an improved algorithm with multi-algorithm advantage integration and joint optimization for the deficiencies in the BP neural network algorithm, which is fully demonstrated in the nonlinear function simulation. In addition, based on the HA-BP algorithm, this paper has designed anomaly data detection model and variable factor analysis model, and their reliability and practicality are also fully demonstrated in the nonlinear function simulation in the analytical study of physicochemical reaction mechanism.

Machine Learning for Cardiovascular Disease Risk Assessment: A Systematic Review

Accurate diagnosis and early detection of heart disease can help save lives because it is the primary cause of mortality. If a forecast is inaccurate, patients could potentially suffer significant harm. Today, it is challenging to predict and identify heart disease. 24 hour monitoring is not practical due to the extensive equipment and time required. Heart disease treatments can be both expensive and challenging. In order to obtain the data from databases and use this information to successfully forecast cardiac illness, a variety of data mining techniques and machine learning algorithms are now accessible. We have used every technique to put the heart disease prognosis into practise. The algorithms used in SVM, NAIVE BAYER, REGRESSION, KNN, ADABOOST, DECISION TREE, and XG-BOOST And Voting Ensemble Method.

Suicidal Behavior Prediction and Socioeconomic Suicide Indicators

INTRODUCTION: According to the WHO (World Health Organization), nearly 0.8 million people commit suicide each year, with more than 20 suicide attempts for every self-immolation. Suicidal behaviors have a profound effect on communities, societies, families, friends, and colleagues. After the recognition of public health as a priority by the WHO, various studies are being conducted to prevent it.OBJECTIVES: The investigation's goals were to improve understanding of suicide by identifying socioeconomic indicators correlated with rising suicide rates among divergent legions globally and to develop a prediction model for those who are at a higher risk of suicide by using different predictors of suicide such as tension, depression, anxiety, and so on.METHODS: We used a variety of data mining techniques to create a prediction model for suicide, including Logistic Regression, Multilayer Perceptron, Polynomial/Gaussian/Sigmoid SVM, Decision Tree, and K-Nearest Neighbors. For identifying socioeconomic suicide indicators, we used various descriptive and exploratory analysis techniques such as mean, regression, and correlation.RESULTS: Classification through the Gaussian Kernel - SVM has been shown to have the best results relative to others. Results also stated that many countries saw a decrease in suicide rates between 2006 and 2015, compared to 1996 to 2005. The highest concentrations have been reported in Europe, while the lower has been observed in South America.CONCLUSION: Things are improving, at least according to the statistics. The performance of Gaussian Kernel-SVM has been demonstrated to be superior to the other algorithms for suicide prediction. Data on suicide and suicide attempts are imprecise and difficult to gather. Suicide and suicide attempt monitoring, and surveillance must be improved for suicide prevention initiatives to be effective.

Use of data mining of to create of a fraud prevention and detection system in credit card

After defining the problem, we'll examine the various techniques (pre-and post-dynamic) for managing installment card fraud recognition, as well as collect all the data from various elements of card-based monetary fraud (regions, fraudulent activity, etc.). When all of the information has been gathered, it's time to put it into practice. It is best to avoid using data that has been gleaned from several sources because it has little practical use until it can be combined. For this collaboration, we're looking into novel data-combination approaches that can help us find the location of financial fraud. The Fraud Prevention and Detection systems were developed utilizing a variety of data mining techniques, including rule learning, rule generation, and rule selection.

Plagiarism Detection through Data Mining Techniques

Plagiarism is a problem that is becoming more prevalent as technology advances and the use of computer systems grows in comparison to previous generations. Plagiarism is the unauthorized use of another person’s work. Since manual plagiarism detection is difficult, this method should be automated. Plagiarism detection can be done using a variety of methods. Some of the research focuses on intrinsic plagiarism, while others focus on extrinsic plagiarism. Data mining is an area that can assist in both detecting plagiarism and improving the reliability of the operation. Plagiarism can be detected using a variety of data mining techniques. Text mining, clustering, bi-grams, tri-grams, and n-grams are some of the techniques that can assist with this. In this paper we will use the data mining techniques to increase the efficiency of detection of plagiarism.

CatBoost — An Ensemble Machine Learning Model for Prediction and Classification of Student Academic Performance

In every educational institution, predicting pupils’ performance is a vital responsibility. Due to this, a variety of data mining techniques, such as clustering, classification, and regression, are applied to anticipate the learner’s study behavior. By Machine Learning’s arrival, it has become vital to forecast students’ academic achievement, and this study attracts significant attention within the scientific community. In addition, the findings from this work have tremendous socio-economic consequences. One area of major research in the world of education today is educational data mining, which is the study of techniques to reveal hidden patterns in educational data. Data mining strategies succeed or fail to depend on the type and quality of the data that is being mined. Here, we provide a novel method that enhances the accuracy of prior student performance prediction by identifying and providing an explanation as to why it is rising. Using our robust machine learning ensemble models, we propose and evaluate a prediction model. The findings demonstrate that our CatBoost — an ensemble machine learning model — is superior to standard machine learning models with an accuracy of 92.27%. This new model was able to show itself to be dependable by the use of smote and hyperparameter optimization, which proved to be valuable methods and approaches. Additional features are significant as well. More critically, a unique method is utilized to increase model transparency. The SHAP values are a valuable part of the student performance prediction system, which we think should be integrated. For those educators tasked with using prediction models in education, we have found that there is a preference for models that offer both insightful insights and easy to understand predictions, as by utilizing our experiment the educator will be able to identify those students who are at early risk and inspire and encourage these students in a positive way.

A New Ensemble Approach to Predict Breast Cancer

The primary objective of using a variety of Data mining techniques in health care domain is to construct a useful model that can effectively interpret the data from a cluster of medical datasets. To reveal the hidden pattern in data, Data Mining techniques and algorithms rely on a wide variety of machine learning techniques. Classification along with prediction techniques play an essential role in medical decision making. This type of knowledge-based system can aid doctors in predicting the disease accurately. The main objective of this paper is to create an ensemble of classification algorithms to classify the cancer data set with higher classification accuracy when compared with the existing classification algorithms available in the literature.

Approach to Integration of Data Mining Techniques in Simulation Results Analysis

The research is carried out in the area of analysis of simulation results by using data mining techniques. The goal of the research is to explore the applicability of data mining techniques in the area of simulation results analysis, to offer an application scheme of data mining techniques in the analysis of simulation results, as well as to demonstrate the usage of these techniques in the analysis of experimental data. As a result of the theoretical study, an approach is proposed, consisting of two stages and combining the fundamental techniques of data farming and knowledge discovery. A variety of data mining techniques, such as correlation analysis, clustering and several visualization mechanisms of results, are used for knowledge discovery. The proposed approach is applied to the analysis of experimental data. The performance of a queueing system is analysed, and knowledge and decision rules are obtained from simulation results.

A Data mining Technique for Analyzing and Predicting the success of Movie

In real world prediction models and mechanisms can be used to predict the success of a movie. The proposed work aims to develop a system based upon data mining techniques that may help in predicting the success of a movie in advance thereby reducing certain level of uncertainty. An attempt is made to predict the past as well as the future of movie for the purpose of business certainty or simply a theoretical condition in which decision making [the success of the movie] is without risk, because the decision maker [movie makers and stake holders] has all the information about the exact outcome of the decision, before he or she makes the decision [release of the movie]. With over two million spectators a day and films exported to over 100 countries, the impact of Bollywood film industry is formidable We gather a series of interesting facts and relationships using a variety of data mining techniques. In particular, we concentrate on attributes relevant to the success prediction of movies, such as whether any particular actors or actresses are likely to help a movie to succeed. The paper additionally reports on the techniques used, giving their implementation and utility. Additionally, we found some attention-grabbing facts, such as the budget of a movie isn't any indication of how well-rated it'll be, there's a downward trend within the quality of films over time, and also the director and actors/actresses involved in the movie.

Challenges for the Implementation of Credit Scoring in Albania

Increased loan availability and competition between lenders have created the need for credit information exchange. The supervisory institutions, the World Bank, the IMF, the Ministry of Economy and Lenders see it as necessary to establish a Credit Information System (CIS) to help manage credit risk. Credit Information System CIS, collects data from various sources and provides credit information to consumers for diversified uses such as to predict their future behavior, also CIS reduces the effect of asymmetric information between borrowers and lenders, facilitating problems Of unfavorable selection and moral hazard, also reduces the monopoly effect of banks' loans by providing incentives for borrowers to repay their loans on time. CIS collects personal information about individuals, their financial records, and alternative data for individuals from a variety of sources called data providers that are usually creditors, lenders, utilities, debt collection agencies, and courts (i.e. Public records etc.). Data providers report their payment experience with the customer in the CIS credit information system. The data provided by data providers, as well as collected by SIK, are then aggregated into the SIK warehouse. Credit punishment is not limited to banks or credit institutions, but also to other institutions such as mobile phone companies, insurance companies, leasing companies, and so on. Credit scoring models are based on statistical techniques, including a variety of data mining techniques, always considering completing according to there is no general technique known as the best statistical technique in building scoring models. This paper presents the effects that affect the current effect of the Credit Information System (CIS) in the Albanian reality in order to reduce credit installment delays during the credit cycle in the banking sector in Albania. There are a number of problems with bad credit for borrowers, as well as debts on lenders. From a lender's performance analysis one of the main causes is the lack of information exchange in the lending market. Also, the credit information system acts as a mediator and regulator of asymmetric information and also to increase transparency in the lending market. In the interest of all stakeholders in Albania (financial institutions, supervisory institutions, government, consumers, etc.) towards financial stability and economic growth in Albania, CIS becomes increasingly necessary towards the consolidation and maintenance of a sound and sound financial system. Credit scoring as a product of CIS through the application of data mining techniques is a growing trend. The decision tree, basic classification rules, expert systems, and any other techniques obtained outside the mini graph techniques and various hybrid combinations are usable and welcome in the scoring industry in the banking sector due to their explicit acceptance/rejection conditions of applicants. Selected literature addresses the challenges faced by banks' lending practices and the role of the Credit Information System (CIS). The growth in demand for loans has led to the need for more formal and more objective methods (generally known as credit scoring) to help credit providers decide whether to grant loans to a borrower, through technology advancement Computer and exponential database growth. In some research it is noted that based on information from some countries around the globe, it is concluded that the existence of credit registers is linked to increased lending volume, lending to business, improved access to finance and a more stable banking sector. The same situation is also presented for Albania, according to this paper.

Data Mining Problems Classification and Techniques

Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

A NEW STAD MODEL TO PREDICT THE DIABETES MELLITUS

Diabetes-mellitus refers to the metabolic disorder that happens due to less insulin secretion action. It is characterized by hyperglycemia. The persistant hyperglycemia of diabetes leads to damage, malfunction and failure of different organs such as kidneys, eyes, nerves, blood vessels and heart. Detection and diagnosis of diabetes at an early stage is the need of the day. Diabetes disease diagnosis and interpretation of the diabetes data is an important classification problem. A variety of data mining techniques are used to discover new patterns of disease and promote the early detection and diagnosis of complex diseases such as diabetes Rule extraction is on among them. The rules are extracted from the dataset. The extracted rules may not only be highly accurate, but also simple and easy to understand. Therefore in this study, The rule extraction algorithm Enhanced STAD model is proposed to achieve highly accurate, concise, and interpretable classification rules for the pima Indian diabetes(PID) dataset, which comprises 768 samples with two classes(diabetes or non-diabetes) and eight attributes. The advanced decision tree algorithm is generated and used for classification. STAD model achieved substantially better accuracy and provided a considerably fewer average number of rules and antecedents. These results suggest that proposed algorithm, is more suitable for medical decision making including the diagnosis of all type of diabetes mellitus.

토너먼트 기반의 빅데이터 분석 알고리즘

모든 데이터는 그 자체로 가치를 가지고 있지만, 실세계에서 수집되는 데이터들은 무작위적이며 비구조화되어 있다. 따라서 이러한 데이터를 효율적으로 활용하기 위해서 데이터에서 유용한 정보를 추출하기 위한 데이터 변환과 분석 알고리즘들을 사용하게 된다. 이러한 목적으로 사용되는 것이 데이터 마이닝이다. 오늘날에는 데이터를 분석하기 위한 다양한 데이터 마이닝 기법뿐만 아니라, 대용량 데이터를 효율적으로 처리하기 위한 연산 요건과 빠른 분석 시간을 필요로 하고 있다. 대용량 데이터를 저장하기 위하여 하둡이 많이 사용되며, 이 하둡의 데이터를 분석하기 위하여 맵리듀스 프레임워크를 사용한다. 본 논문에서는 단일 머신에서 동작하는 알고리즘을 맵리듀스 프레임워크로 개발할 때 적용의 효율성을 높이기 위한 토너먼트 기반 적용 방안을 제안하였다. 본 방법은 다양한 알고리즘에 적용할 수 있으며, 널리 사용되는 데이터 마이닝 알고리즘인 k-means, k-근접 이웃 분류에 적용하여 그 유용성을 보였다. While all of the data has a value in itself, most of the data that is collected in the real world is a random and unstructured. In order to extract useful information from the data, it is need to use the data transform and analysis algorithms. Data mining is used for this purpose. Today, there is not only need for a variety of data mining techniques to analyze the data but also need for a computational requirements and rapid analysis time for huge volume of data. The method commonly used to store huge volume of data is to use the hadoop. A method for analyzing data in hadoop is to use the MapReduce framework. In this paper, we developed a tournament-based MapReduce method for high efficiency in developing an algorithm on a single machine to the MapReduce framework. This proposed method can apply many analysis algorithms and we showed the usefulness of proposed tournament based method to apply frequently used data mining algorithms k-means and k-nearest neighbor classification.

A Semantic Network Analysis of The Network Neutrality Debate

With nearly 4 million remarks, the 2014 proceeding that produced the recently released Open Internet Order stands as the most commented Federal Communications Commission (FCC) docket to date. According to FCC Chair Tom Wheeler, the volume and substance of this public input was crucial in shifting the agency’s final rules away from legal justifications based on Section 706 towards Title II reclassification. Mass public sentiment had a significant impact on agency decision-making. The bulk release of comments thus gives communication researchers an opportunity to more fully understand the formation and expression of public opinion. Using a variety of proven data mining and semantic network analysis techniques, this paper will conduct an exploratory but comprehensive quantitative inquiry of the comments.Previous quantitative surveys have been enveloped in controversy. In particular, the Sunlight Foundation’s analysis sparked a number of responses and counter analyses by involved think tanks and activists, requiring further clarification by the organization and an official response by the FCC. By first reproducing and then extending this work, this paper will serve as a first step in establishing an official understanding of the public’s input.This paper is divided into four sections. The first section provides a literature review of the relevant research from both psychology and communication theory that undergirds semantic network analysis. The second section seeks to answer a number of key questions that still remain after the initial round of research. For example, how many comments mentioned and then supported Title II reclassification? Of the total submitted comments, how many were due to form submissions? From which organizations did these form submissions come? How many of the comments were not related to network neutrality but still expressed general concern with the state of the American broadband industry? Moreover, just how many of the comments were not related to any broadband concern?The third will employ a variety of data mining techniques including word-pair link strength and k-nearest neighbors algorithms to chart changes in semantic networks as a quantitative proxy for changing public opinion. By parsing comments into a number of time series, changes between the two sets can be tracked. Four time sets have been identified. The first will compare comments during the first and last 30 days of the official comment period. The second time series will explore the influence of popular television host John Oliver. His widely shared and viewed TV segment ended with a call for viewers to file comments in the proceeding that immediately crashed the FCC comment system. By comparing those comments filed 30 days after and 30 days before the show ran, a measure of his influence on the conversation will be established. Next, the importance of Internet Slowdown Day, an event popularized by a number of activist groups will undergo analysis through a similar 30 day before and after comparison. Lastly, all of the comments in the initial round of comments will be compared to those of the reply round. The fourth and final section will review conclusions from the research and outline opportunities for future studies.

Open issues for partitioning clustering methods: an overview

Over the last decades, a great variety of data mining techniques have been developed to reach goals concerning Knowledge Discovery in Databases. Among them, cluster detection techniques are of major importance. Although these techniques have already been largely explored in the scientific literature, there are at least two important open issues: the existent algorithms are not scalable for large high‐dimensional datasets, and the unsupervised nature of traditional data clustering makes it very difficult to generate meaningful clusters. This article presents an overview of the strategies being explored in order to deal more deeply with these issues. Moreover, it describes a new semi‐supervised clustering strategy that exemplifies the integration of several approaches and that can be employed with partitioning algorithms, such as PAM and Clarans. The technique addresses an improvement to these types of algorithms, which is obtained by using must‐link feedback information provided by the users in an interactive and visual environment. WIREs Data Mining Knowl Discov 2014, 4:161–177. doi: 10.1002/widm.1127This article is categorized under: Technologies > Structure Discovery and Clustering

Simulating adverse event spontaneous reporting systems as preferential attachment networks: application to the Vaccine Adverse Event Reporting System.

Spontaneous Reporting Systems [SRS] are critical tools in the post-licensure evaluation of medical product safety. Regulatory authorities use a variety of data mining techniques to detect potential safety signals in SRS databases. Assessing the performance of such signal detection procedures requires simulated SRS databases, but simulation strategies proposed to date each have limitations. We sought to develop a novel SRS simulation strategy based on plausible mechanisms for the growth of databases over time. We developed a simulation strategy based on the network principle of preferential attachment. We demonstrated how this strategy can be used to create simulations based on specific databases of interest, and provided an example of using such simulations to compare signal detection thresholds for a popular data mining algorithm. The preferential attachment simulations were generally structurally similar to our targeted SRS database, although they had fewer nodes of very high degree. The approach was able to generate signal-free SRS simulations, as well as mimicking specific known true signals. Explorations of different reporting thresholds for the FDA Vaccine Adverse Event Reporting System suggested that using proportional reporting ratio [PRR] > 3.0 may yield better signal detection operating characteristics than the more commonly used PRR > 2.0 threshold. The network analytic approach to SRS simulation based on the principle of preferential attachment provides an attractive framework for exploring the performance of safety signal detection algorithms. This approach is potentially more principled and versatile than existing simulation approaches. The utility of network-based SRS simulations needs to be further explored by evaluating other types of simulated signals with a broader range of data mining approaches, and comparing network-based simulations with other simulation strategies where applicable.

The Six Technical Gaps between Intelligent Applications and Real-Time Data Mining: A Critical Review

Intelligent application is characterized by its ability to make decision autonomously. Some examples are distributed agents that collaboratively complete a complex task, yet each one of them is able to work and reason independently given the dynamic situation where they are in. The underlying think tank is often a collection of core mechanisms that include environment sensing, data capturing, data mining, ETL, knowledge processing, and decision making etc. All these techniques when placed and function together as a whole intelligent system, they will have to fulfill stringent deadlines imposed by the requirements of a real-time system. In the literature many research papers can be found on a wide variety of data mining techniques that enable intelligent applications operating in real-time. This report offers a critical review of the relevant literature, and contributes to the knowledge of identifying their shortcomings, so-called the technical gaps between the real-time requirements of a general intelligent system and the supporting components. A discussion follows on the possibilities of future intelligent applications that are empowered by data stream mining.

Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects

Often, the explanatory power of a learned model must be traded off against model performance. In the case of predict-ing runaway software projects, we show that the twin goals of high performance and good explanatory power are achievable after applying a variety of data mining techniques (discrimination, feature subset selection, rule covering algorithms). This result is a new high water mark in predicting runaway projects. Measured in terms of precision, this new model is as good as can be expected for our data. Other methods might out-perform our result (e.g. by generating a smaller, more explainable model) but no other method could out-perform the precision of our learned model.