Predicting IT employability using data mining techniques

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.

Classification is a machine learning procedure that tags data instances into predefined class labels which are used to predict the data according to those Classes. Many classification algorithms in data mining have been stated, such as: C4.5, Apriori, Genetic algorithm, and Fuzzy set approaches which mainly uses heuristic or greedy search to get frequent sets in data for classification, resulting in high error ratio. Recently, a new method for classification has been proposed, called the Classification Using Association also known as associative classification. The main purpose for this is to mine class-association rules. Associative Classification has more advantages than the heuristic and greedy method, as it easily removes noise and higher accuracy is obtained. It additionally generates a rule set, that are more complete than traditional classification methods. This paper presents a meticulous survey on various Associative classification techniques. Moreover, a comparative analysis of accuracy and efficiency of those methods is presented.

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

Credit decision-making is a vital process in the banking sector as it helps to reduce losses by identifying non-creditable individuals. Classification algorithms in data mining provide accurate results in the aforementioned area. But, such real-world lending environments require classification results to be easy to interpret. The lack of explicability of several existing classifiers makes banks reluctant in using them. An ideal classifier needs to be accurate with interpretability encapsulated within it. Decision trees are accurate, but for large datasets, the tree becomes very large and may not be comprehensible. Genetic programming (GP) is widely applied for solving classification problems since it can produce smaller trees by using tree-size, as fitness measure or by depth-limiting the trees. Hence, we propose an algorithm named GPeCT that merges decision tree and GP to produce a near-optimal decision tree classifier. We demonstrate the performance of GPeCT through experiments on large datasets from banking and other domains.

Credit decision-making is a vital process in the banking sector as it helps to reduce losses by identifying non-creditable individuals. Classification algorithms in data mining provide accurate results in the aforementioned area. But, such real-world lending environments require classification results to be easy to interpret. The lack of explicability of several existing classifiers makes banks reluctant in using them. An ideal classifier needs to be accurate with interpretability encapsulated within it. Decision trees are accurate, but for large datasets, the tree becomes very large and may not be comprehensible. Genetic programming (GP) is widely applied for solving classification problems since it can produce smaller trees by using tree-size, as fitness measure or by depth-limiting the trees. Hence, we propose an algorithm named GPeCT that merges decision tree and GP to produce a near-optimal decision tree classifier. We demonstrate the performance of GPeCT through experiments on large datasets from banking and other domains.

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.

Use of machine learning (ML) and data mining (DM) algorithms has surfaced more often in the recent years for disease risk prediction problems in the healthcare communities. Several traditional feature selection models are combined with the DM and ML algorithms to improve accuracy of the disease risk prediction. In this study, a new Bio-inspired Ensemble Feature Selection (BEFS) model is introduced which is applied with the DM and ML algorithms. In the BEFS model, the most relevant and highly contributing features in the prediction are determined with a bio-inspired algorithm i.e., genetic algorithm, and an ensemble algorithm i.e., random forest algorithm. These important features obtained from the proposed model are then combined in various combinations and applied with the DM and ML algorithms, here logistic regression (LR) and random forest (RF), and the results obtained are promising. The experiment is executed using the famous ML language R. To accomplish this objective, the Breast Cancer Wisconsin (Diagnostic) dataset of UCI (University of California, Irvine) ML repository is utilized. In the experimental outcomes, the highest accuracy attained with the BEFS model is 96.49%, the AUC (Area Under Curve) achieved is 96%, and the sensitivity is 98.11%. These results, which greatly improve the disease risk prediction, are higher than several other existing works, while utilizing only six most relevant features out of the thirty two features of the dataset.

SummaryOne of the most important challenges of African countries is the effective use of information and communication technologies and its generalization in the different sectors (education, economic, political, and so on). This will have a great impact on different aspects of society and economic activities by making everyday procedures easier and more efficient. In this same context, we are interested in this article by the proposition of a decision support framework based on the use of data mining (DM) techniques. Using DM in e‐government is the process of translating data to appropriate knowledge which can be useful for decision‐making. We propose a framework whose goal is the generation of association rules (ARs) for better decision‐making. This framework includes two approaches: (1) the first approach applies different DM algorithms and (2) the second approach optimizes the first one by considering two different metaheuristics: the Genetic algorithm and the Cuckoo search algorithm. A new relevance measure called “Weighted Dominance” has been considered to evaluate the quality of the generated ARs. Extensive experiments have been conducted using different datasets. The results obtained demonstrated the effectiveness of combining DM and optimization algorithms compared to the first approach. Finally, a case study related to an Algerian mobile phone company has been presented illustrating the use of our framework in the decision‐making process.

Cardiac arrest is a potentially fatal loss of heart function that occurs suddenly and without warning. Predicting cardiac arrest early could increase the likelihood of survival and allow for prompt treatment. The discipline of computer science known as “datamining” focuses on the process of gleaning useful information from massive databases. Algorithms for data mining can be used to look for trends in records that can indicate a cardiac arrest. Patients at high risk of cardiac arrest due to their medical history, lifestyle choices, or other variables can be pinpointed, for instance, with the help of data mining algorithms. Prediction of cardiac arrest using data mining techniques is discussed in this research. The chapter talks about the many data mining methods that have been employed for this, and the studies that have evaluated their efficacy. The chapter also covers the difficulties of employing data mining for early prediction of cardiac arrest, as well as potential future research avenues.

This paper describes an approach to identify the state of a power system using Data Mining Algorithms. With the advent of Phasor Measurement Units (PMU), a huge amount of power system data is being stored day-to-day in the Phasor Data Collectors (PDCs). Knowledge discovery and machine learning techniques can make use of this data to extract valuable information and interesting patterns in these databases. In this paper, Symbolic Aggregate ApproXimation (SAX) is used to convert the time stamped phasor data from the PMUs into symbolic strings and Data Mining (DM) algorithms are used to predict the current state of a power system. Along with the state, the location and cause of disturbance is also identified in minimal time. The effectiveness of different DM algorithms for determining state of a power system is also shown using the measures of accuracy, precision and recall.

Identification of potential injury risk factors and construction of prediction models for athletes using data mining algorithms involve analyzing various factors such as biomechanical, physiological, and environmental variables to determine their correlation with injury occurrences. Data mining techniques, such as decision trees, logistic regression, or neural networks, are then applied to identify patterns and relationships within the data. By integrating this information, predictive models can be developed to forecast the likelihood of athletes sustaining injuries based on their individual characteristics and training conditions. This paper introduces a novel approach for athlete injury risk prediction using the Noninvasive Data Mining Model with Multi-relational and Multi-dimensional Clustering (Ni-DMMMC) algorithm. By leveraging advanced data mining techniques, Ni-DMMMC aims to identify hidden relationships between various athlete characteristics, including biomechanical risk, training load, physiological markers, and environmental factors, to predict injury susceptibility accurately. Through multi-relational and multi-dimensional clustering, the algorithm effectively categorizes athletes into distinct clusters based on their unique risk profiles. These clusters provide valuable insights into the diverse injury risk profiles present within athlete populations, enabling targeted intervention strategies and personalized athlete management approaches. The proposed approach holds significant promise for enhancing injury prevention efforts, optimizing athlete performance, and improving overall well-being in sports medicine practice. athletes in Cluster 1 exhibit high biomechanical risk (0.85), moderate training load (0.60), low physiological markers (0.25), and moderate environmental risk (0.45), while athletes in Cluster 2 display moderate biomechanical risk (0.70), high training load (0.90), high physiological markers (0.80), and low environmental risk (0.30). These numerical values serve as indicators for assessing an athlete's injury risk profile comprehensively.

In software defect prediction systems, choosing the best data mining classification algorithm is essential for efficient decision-making. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for ranking classification algorithms according to several criteria is assessed in this study. Classifiers were evaluated and ranked using the AR1 and JM1 datasets using a structured multi-criteria decision-making (MCDM) methodology. According to the results, Lazy-IBK continuously performs better than other classifiers, demonstrating how well TOPSIS finds the top-performing models. Furthermore, the comparative study of classification algorithms shows how reliable the approach is when taking into account variables like scalability, accuracy, and computational efficiency. According to the results, TOPSIS considerably enhances classifier selection when it comes to software defect prediction, resulting in data mining applications that make better-informed and impartial decisions. Key Words: MCDM, Software Defect Prediction, TOPSIS

Predictive analytics has emerged as a powerful tool in the field of financial engineering, enabling organizations to forecast trends, optimize strategies, and mitigate risks. Data mining techniques play a crucial role in extracting meaningful patterns from large volumes of financial datasets, contributing to the development of predictive modeling techniques. The purpose of this paper is to explore the application of various data mining techniques in predictive analytics within the financial sector, with a focus on their effectiveness in risk management, fraud detection, and portfolio optimization. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are increasingly being employed to model complex financial systems and enhance predictive accuracy. Additionally, techniques like clustering, association rule mining, and time series analysis are used to uncover hidden relationships and temporal patterns in financial datasets. Despite the significant advancements in these techniques, challenges remain in terms of data quality, computational complexity, and interpretability of results. This paper addresses these challenges and provides a comprehensive review of the most widely used data mining methods in financial engineering. Furthermore, it discusses the potential for future advancements in data mining algorithms and their integration with other emerging technologies like artificial intelligence and big data analytics. The findings indicate that while data mining offers significant potential in financial analytics, careful consideration must be given to the selection of appropriate techniques and the proper handling of data to ensure reliable predictions. Ultimately, this paper aims to contribute to the ongoing development of robust predictive modeling techniques in financial engineering by emphasizing the importance of data mining as an indispensable tool.

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.