A Review on Comparative Analysis of Data Mining-Algorithms and 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.

Students currently mostly experience psychological issues like worry and fear, which are primarily brought on by the high demands placed on them. One psychotherapy technique is music therapy. The goal is to use music to enhance health, particularly as a tool to break down barriers both inside and outside the body. Based on data mining (DM) technologies, this paper examines the impact of music education on students' psychological health. The study demonstrates that the DM algorithm utilised in this work has the lowest error rate, with an average error rate of only 6.90%, followed by the ACA method with an average error rate of 17.48%, and finally the AI algorithm with an average error rate of 29.35%. As can be shown, this approach is more suited to research the effects of music instruction on students' psychological well-being. The functional module based on DM is developed through simulation experiments to confirm the application effectiveness of the DM algorithm. This is done by using the data source of DM and the structural model of the mining system to build this module on the foundation of the original psychological evaluation system.

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.

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.

This paper expounds the fundamental theory of data mining and customer segmentation, and studies on the specific implementation of data mining processes and technology. We put forward the customer segmentation application model based on data mining technology, which is applied in the retailing industry to carry out a empirical study.

As a confluence of data mining and World Wide Web technologies, it is now possible to perform data mining on Web log records collected from the Internet Web-page access history. The behaviour of Web page readers is imprinted in the Web server log files. Analyzing and exploring regularities in this behaviour can improve the system performance, enhance the quality and delivery of Internet information services to the end user, and identify populations of potential customers for electronic commerce. Thus, by observing people using collections of data, data mining can bring a considerable contribution to digital library designers. In a joint effort between the TeleLearning-NCE (Networks of Centres of Excellence) project on the Virtual University and the NCE-IRIS project on data mining, we have been developing a knowledge discovery tool, called WebLogMiner, for mining Web server log files. This paper presents the design of WebLogMiner, reports current progress and outlines future work in this direction.

As a budding technology, big data’s technical implementation and commercial application are in the exploratory stage. With the increasing development of network and communication technology, a large amount of information is pouring in. How to effectively select the required information has become a more and more prominent problem. Data mining is a data processing technology developed to meet this need. Support vector machine is a new technology in data mining. It is a new tool to solve machine learning problems with the help of optimization methods. Among them, it focuses on the support vector machine, including the development history and present situation of support vector machine, the main basic concepts and research contents. On this basis, it studies various training algorithms of support vector machine which are relatively common at present, and compares their advantages and disadvantages. Big data has various data types, forming a data stream with various attributes. As we all know, data source classification based on batch processing can improve the query speed, but it still can’t meet the demand of real-time query. Therefore, feature selection mechanism is usually introduced in the process of data mining modeling to reduce its load. However, when faced with the query of high-dimensional data, the query space grows exponentially, which is difficult to realize. Therefore, this paper proposes the efficiency of an intelligent acceleration algorithm for big data mining based on vector machine communication network.

Data mining approaches are designed for classification problems in which each observation is a member of one and only one class. In this study, a non-deterministic approach based on C5.0 data mining algorithm has been employed for discriminating the phenological stages of maize from emergence to dough, in a field located in Karaj, Iran. Two readily-available predictors i.e. accumulated growing degree days (AGDD) and multi-temporal LANDSAT7-extracted normalized difference vegetation index (NDVI) was used to build the decision tree. The AGDD was calculated based on three cardinal thresholds of temperature i.e. effective minimum, optimum, effective maximum. The NDVI was compared with two recently developed indices namely, enhanced vegetation index2 (EVI2) and optimized soil adjusted vegetation index (OSAVI) using the signal to noise ratio (SNR) criterion. Findings confirmed that these three remotely sensed indices do not have significant differences, therefore, the smoothed time series of NDVI was used in the C5.0 algorithm. The precisions of classification by C5.0 data mining algorithm in partitioning of training and testing data were approximately 90.51 and 81.77%, respectively. The mean absolute error (MAE) values of the onset of maize phenological stages were estimated about 2.6–5.3 days for various stages by C5.0 model. While corresponding values for the classical AGDD model were 3.9–10.7 days. This confirms the skill of data mining approach in comparison with commonly-used the classical AGDD model in applications of real time monitoring.

Intelligent computing system based on pattern recognition and data mining algorithms

Objectives: Based on data mining technology, this research explores the rules of traditional Chinese medicine treatment of insomnia and obtains some significant medical implication through data mining. Technology or Method: Nearly 6,000 electronic medical records of Chinese medicine clinics are selected, and data pre-processing is performed. Then, Aripori's data mining algorithm is used to analyze prescription medication rule, and the association between symptoms and drugs. Finally, a network diagram is built through complex network analysis. Results: There are 631 insomnia cases that met the criteria, 22 typical symptoms, and 330 drugs. There are 6 symptom association items with a confidence level of more than 30%, 46 drug association items with a confidence level of more than 75%, and 40 symptom association rules with a confidence level of more than 40%. Conclusions: The results showed the rule of medication used in the diagnosis and treatment of insomnia in Chinese medicine outpatients, and found the association between prescription medication rules and symptomatic drugs. Clinical or Biological Impact: This paper provides a reference to the clinical diagnosis and treatment of young physicians, and provides a reference to the development of new drugs. And to promote the development of the theory and practice of traditional Chinese medicine prescriptions is conducive to inheriting and carrying forward the experience and advantages of Chinese medicine treatment of insomnia.

This paper conducts a methodological research on the academic performance of online learners. This paper applies classification models commonly used in data mining algorithms, such as random forests based on decision trees, support vector machines, neural networks, K nearest neighbor algorithms, etc., combined with data mining tools S software and statistical analysis tools, and analyzes the University of Finance and Economics Tools scores of online learners in the class of 2019. It studies the important factors that affect the academic performance of college students' online learners, and uses these factors to predict students' academic performance. Based on the distance calculation method in mathematics, the article separately studied the application of Euclidean distance correlation analysis algorithm and correlation coefficient correlation algorithm in curriculum relevance, and compared several correlation algorithms. Experimental research results show that in the era of big data, learners will accumulate a large amount of structured and unstructured data during online learning. We can explore the influencing factors of online learners' academic performance through data mining technology, and we can also use machine learning to automatically learn from the data to the academic performance prediction model.

The thesis starts from the basic theory, introduces the method of physical data acquisition and the concept of data mining. Marketing evolves from traditional marketing schemes to data mining, which leads to the relationship between data mining technology and modern market management. It focuses on the method of obtaining user information and data mining technology in "cross-marketing", "integrated marketing", and "integrated marketing". Applications in "real-time marketing", "green marketing", "relationship marketing", and "network marketing". At the same time, combined with case analysis, the comprehensive application of data mining in "shopping basket analysis" is discussed and elucidated from the theoretical and technical level.

The purpose is to enhance the timeliness of news, provide real-time news for the audience, and attract more traffic for the news media, thereby helping news media grow financially and strengthening the network security communication efficiency and accuracy. First, an intelligent real-time image acquisition system is introduced, and its principle and advantages are analyzed. Second, an intelligent real-time image acquisition model based on DL (Deep Learning) algorithm is established by combining DM (Data Mining) technology. Finally, the application of an intelligent image acquisition system in network communication is adjusted. The research results show that the combination of DL intelligent real-time image acquisition system with DM technology can effectively deal with various network communication scenarios, enhancing the accuracy and timeliness of network communication. The AlexNet model performs excellently in all aspects, with or without data enhancement or equalization. The recognition rate reaches 99.6% after data equalization and enhancement, and the AlexNet model can accurately classify news and non-news sceneries. This shows that the application of an intelligent real-time image acquisition system based on DM technology can effectively enhance the real-time performance of network communication. The results set an example for the application of relevant intelligent image acquisition technology in the journalism industry.

This article has been retracted from publication in the Taylor & Francis journal, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Following publication, concerns were raised by multiple third-parties around the content of the special issue and the decision-making process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that this article included in Special Issue titled “ Envisage Computer Modelling and Statistics for Agriculture”, guest edited by Gunasekaran Manogaran was not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines.