Introduction to 3DM: Domain-Oriented Data-Driven Data Mining

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.

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.

In this digital age, businesses that collect consumer information provide better service to its customers or new market areas in the name of consumer information from the open mining, artificial intelligence techniques, methods and algorithms in conjunction with data mining are used in order to be able to get the results of these operations.Data mining methods in an intensive competition environment in the service-producing process, are being used effectively. For customer relations in the context of these techniques in improving library processes and in improving the quality of service data mining method is utilized.The data aim-based of this study was to set a specific time range of the data that is received from a public University. Study of the library data in question required data mining methods of clustering and the apriori algorithm.As a result, the library data are implemented on data mining algorithms in the library with the given service processes and improvement of the quality of these services being the defining periodic changing of profile reader to reader demands and determined display and readers library interactions. Subsequent studies based on data from different universities library may lead too to different research, comparisons, and therefore different results. At the same time, the results of repeated studies for the readers of different library can be evaluated. İn similar studies, the amount of data and the range of time can be effective on the results too.

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.

Background/aimIn temporal lobe epilepsy (TLE), brain positron emission tomography (PET) performed with F-18 fluorodeoxyglucose (FDG) is commonly used for lateralization of the epileptogenic temporal lobe. In this study, we aimed to evaluate the success of quantitative analysis of brain FDG PET images using data mining methods in the lateralization of the epileptogenic temporal lobe. Materials and methods Presurgical interictal brain FDG PET images of 49 adult mesial TLE patients with a minimum of 2 years of postsurgical follow-up and Engel I outcomes were retrospectively analyzed. Asymmetry indices were calculated from PET images from the mesial temporal lobe and its contiguous structures. The J48 and the logistic model tree (LMT) data mining algorithms were used to find classification rules for the lateralization of the epileptogenic temporal lobe. The classification results obtained by these rules were compared with the physicians’ visual readings and the findings of single-patient statistical parametric mapping (SPM) analyses in a test set of 18 patients. An additional 5-fold cross-validation was applied to the data to overcome the limitation of a relatively small sample size. Results In the lateralization of 18 patients in the test set, J48 and LMT methods were successful in 16 (89%) and 17 (94%) patients, respectively. The visual consensus readings were correct in all patients and SPM results were correct in 16 patients. The 5-fold cross-validation method resulted in a mean correct lateralization ratio of 96% (47/49) for the LMT algorithm. This ratio was 88% (43 / 49) for the J48 algorithm.Conclusion Lateralization of the epileptogenic temporal lobe with data mining methods using regional metabolic asymmetry values ​​obtained from interictal brain FDG PET images in mesial TLE patients is highly accurate. The application of data mining can contribute to the reader in the process of visual evaluation of FDG PET images of the brain.

Distribution network has a wide range of business, and different users have different needs in business data. Mining the data of interest of distribution network business personnel can process business more efficiently and improve work efficiency. Focusing on data mining model and data mining algorithm, the project needs data processing before and after mining, including deleting repeated data and data compression, accelerating data processing speed and optimizing platform construction. The application platform can push relevant information and reports according to users’ demands, reduce users’ time in searching grid data, present users’ required information more efficiently, and improve the overall working efficiency and level of network personnel.

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

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.

Based on Information Fusion Technique with Data Mining in the Application of Finance Early-Warning

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.

Objectives: Clustering algorithm is broadly used as spectral algorithm in social media, where a reference of contact is used and mined further for various combinations of suggested friends and lookups. This paper Identifies key lead generation techniques to be used in customer relationship management for sales marketing to decide data gathering. Also to define key merits and demerits of these techniques and to prepare a Matrix of comparison of these techniques to justify the data source and data set. M-Cluster algorithm is used for lead qualification i.e. training set preparation and data evaluation. Methods: Todefine a training set based on various attributes/fields in the data given for classification. This training set is used to run the data process and to produce expected result. This is assessed and accepted for definite data set processing or additional run for interim training data set preparation. Findings: This study is taken to customize clustering algorithm for data mining process in a customer relationship management field as the space of data is more and variant. Also proving the usability of customized clustering algorithm in data mining and the efficiency in processing mechanism compared to other methods used in current situation of data mining in customer relationship management is the part of this study. The customized algorithm developed as part of this study considers the two major areas of data mining using clusters. Applications: The results produced tremendous trends that the clustered algorithm suits to any data mining process when scaling and data classification are diversified and less in control.Keywords: Classification, Clustering Algorithm, Data Mining, K-Means, Lead Generation, M-Clustering Algorithm

Electronic payments is something that is currently in high demand by investors today, but transactions are often constrained due to various problems, especially from third parties. For this reason, cryptocurrency emerged, which is one of the solutions for conducting electronic payment transactions. Some types of cryptocurrency that are most in demand by investors are bitcoin, ethereum, and ripple. The fluctuation value of cryptocurrency is very difficult to predict so that investors often experience losses when making transactions. This study aims to predict cryptocurrency prices such as bitcoin, ethereum and ripple using data mining algorithms. The data mining algorithm used in this prediction process is K-NN, Neural Network, SVM, Linear Regression, Random Forest and Decision Tree. Data mining modeling is done by dividing the dataset into each type of commodity and then analyzed using each algorithm. The results of this study indicate that the accuracy value obtained from some data mining algorithms is good enough to predict cryptocurrency prices

Less than optimal choice of the university department is one of the serious problems Turkish high school students have been suffering.There are a number of potential factors affecting the student's choice of her future profession.Some of these have received attention in the literature, but such studies do not always involve an investigation of the relationship between the factors analyzed and subsequent levels of academic achievement.The present study examines the relationship between the level of academic achievement and the students' abilities, interests and expectations, by using different data mining methods and classifiers, as a preliminary work to develop a system that will guide the student to selecting a career that will be a better match for her in the future.C4.5, SVM, Naive Bayes and MLP algorithms are used for the analysis; 10-fold cross validation and train-test validation are used as models to evaluate the classifiers results.The student feature set is obtained through questionnaires and psychometric tests.The questionnaire and the psychometric test were applied to 210 and 52 students respectively, from the Computer Engineering Department at Cumhuriyet University.The class was labeled either "successful" or "unsuccessful" with reference to the grades received by each student in computer engineering courses.The comparisons of various data mining algorithms, different data set results, and models used are presented and discussed.

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.

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.