3DM: Domain-oriented Data-driven Data Mining

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

The advanced design of a centrifugal compressor with high efficiency and wide operating range is a challenging task due to the complex flow field arising from the three-dimensional geometry, especially for the high-speed, highly loaded centrifugal compressor stage, which typically has a relatively narrow operating range. A great effort has been undertaken recently to solve the time-costly three-dimensional design problem with the assistance of a metamodel. Some effort has been done to gain insight into the design space with the assistance of the data mining method. However, the published works lack any study that systematically performs the data mining between the performance and three-dimensional geometry data due to two unsolved issues, i.e., lack of reliable systematic data mining model and unresolved high-dimensional data problem in the centrifugal compressor community. To tackle these issues, a systematic metamodel-driven data mining (MDDM) model including six general modules (i.e., problem understanding, data understating, metamodeling, data set preparation, knowledge discovery, and deployment) has been proposed and implemented to the knowledge discovery of the well-known Radiver high-speed centrifugal compressor stage. Particular attention has been paid to develop the design principle of operating range extension for the examined high-speed stage. Four specific data mining techniques, i.e., descriptive statistics, self-organization map, k–d tree, and Sobol index, were used for the statistical, correlation, cluster, and sensitivity analysis. The results showed the performance improvement probabilities, the trade-off relationships between efficiency and pressure ratio/operating range, and the characteristic variation of the performance. Specifically, the wide operating range design subspace and the narrow operating range design subspace were split away from the whole design space. In these subspaces, the two most sensitive geometry parameters that controlled the meridional curvature made a large contribution to the stage performance, especially for the meridional curvature at the shroud side near the impeller outlet. The appropriate variation ranges of the two sensitive geometry parameters were recommended, and the flow mechanism behind them was clarified. The statistical results showed that over 90% of the design stages in the recommended variation ranges had a wide operating range. A design case was chosen randomly in the recommended range to verify the performance improvement via computational fluid dynamics (CFD) simulations. The outcomes of this work are particularly relevant for the advanced design of compressors with high efficiency and a wide operating range for flexibility.

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.

In this paper, we propose four data mining models for the Internet of Things, which are multi-layer data mining model, distributed data mining model, Grid based data mining model and data mining model from multi-technology integration perspective. Among them, multi-layer model includes four layers: 1) data collection layer, 2) data management layer, 3) event processing layer, and 4) data mining service layer. Distributed data mining model can solve problems from depositing data at different sites. Grid based data mining model allows Grid framework to realize the functions of data mining. Data mining model from multi-technology integration perspective describes the corresponding framework for the future Internet. Several key issues in data mining of IoT are also discussed.

Data mining research deals with extracting useful information from large collections of data. Since data mining is a complex process that requires expertise, it is beneficial to provide it as a service on the web. On the other hand, such use of data mining services combined with data collection efforts by private and government organizations leads to increased privacy concerns. In this work, we address the issue of preserving privacy while providing data mining services on the web and present an architecture for privacy preserving sharing of data mining models on the web. In the proposed architecture, data providers use APPEL for specifying their privacy preferences on data mining models, while data collectors use P3P policies for specifying their data-usage practices. Both parties use PMML as the standard for specifying data mining queries, constraints and models.

Objective Training monitoring is an important part of scientific training, and also accumulated a large amount of data, but the analysis and evaluation of biochemical indicators are mostly concentrated on the level of experience and the general, phased and individualized research application of statistical methods. The data mining technology is applied to the analysis and evaluation of the biochemical indexes of competitive sports, the analysis of the data is carried out in the deep level, the potential, new and useful information and knowledge are extracted, and the new exploration ideas are carried out for the analysis of the biochemical indexes of competitive sports, and a more reliable and more powerful data branch is provided for the scientific and efficient training support.
 Methods Using the literature data method, logic analysis method and expert interview method, the application of the current data mining technology in the analysis of biochemical indicators is summarized.
 Results The scientific analysis and evaluation of athletes' physical function status has been the focus of domestic and foreign coaches and sports researchers. The application of data mining technology in sports biochemical indicators is also becoming more and more extensive. For example, Mao Jie and others applied the gray ART clustering model analysis method to the monitoring of competitive sports biochemical indicators. Through this data mining model, the coach can easily judge the athlete's competitive physical condition, and can provide a scientific basis for correct training according to the different competitive conditions of each athlete, using different training guidance programs and training methods. Ma Jing et al. explored the feasibility of applying decision tree algorithm and association rules in volleyball biochemical analysis. It was found that C5.0 decision tree and Apriori association rule algorithm can be used to predict and analyze the technical level of women's volleyball players. Li Guangjun and others successfully applied the association rule data mining to the biochemical data analysis of canoeists, and provided a basis for scientific decision-making and analysis of sports training and athlete selection. Zhang Hui designed a data mining system for sports biochemical index based on association rules. The results show that the system has fast data mining rate, short time consuming and high reliability. It provides a more scientific evaluation standard for the data mining of sports biochemical index, and also provides a basis for the future training program.
 Conclusions With the development of competitive sports, in order to achieve new heights, the application of data mining technology to vast biochemical data is of great significance for the establishment of scientific training evaluation methods and standards, and is also the inevitable development of future sports scientific research.

In this paper, data mining technologies are used to analyze and extract features that can distinguish normal activities from intrusions. Based on the common model CIDF, we present an IDS framework with an embedded data mining module to improve accuracy of IDS. Three subsystems (including monitor system, data process system and decision-making system) in the framework are introduced respectively. Using experiments on mining network connection features, we present a decision-tree classification algorithm, which uses data set of network connection features as training data set to build decision tree. Using system behaviors as new samples and testing their attributes on the decision tree can recognize anomalies and unknown intrusions accurately.

The subfield of technologies within the discipline of computer science is known as "data mining" Moreover, statistics are put to use in order to identify trends within the database. Essentially, the primary goal of the data mining methods is to extract from the database any information that will be valuable in the future or that will be needed at some point in the future and transform it into a usable format. Understandable framework for future usage. There are several approaches to choose from, each of which may be helpful in carrying out data mining operations in an effective manner. There is some information accessible in this document concerning comparative studies or analyses of various data mining approaches and certain data mining algorithms. This information may be found in this publication. This means that reading this paper will assist us in gaining knowledge regarding data mining technologies, data mining algorithms, as well as some data of corporations that have upgraded or enhanced their profits by modifying their data mining technologies and have obtained impressive output as a result of doing so.

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.

This paper is to enhance our understanding about the second-order data mining. In particularly, we examine the effect of human cognition on the creation of intelligent knowledge during the second-order data mining process. Prior studies have suggested that human cognition plays an important role in the second-order data mining process during which intelligent knowledge was discovered. Given the knowledge that no single data mining model outperforms others for all problems, a common practice in data mining projects is to run multiple data mining models at first and then invite a group of people to collaboratively make judgments on these data mining models’ performance. These judgments often diverge. Little research exists to explain why these variations of human judgments occur.

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.

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

In recent years, data mining technology has attracted widespread attention. The process of data mining is to quickly discover knowledge that is not obvious but still valuable in the data. This process is an advanced process. The use of DMT (data mining technology) for law extraction, DM (data mining), sorting and analysis can provide an effective decision-making reference. The rapid advancement of technology enables organizations to accumulate large amounts of data. However, extracting useful information has become a huge challenge. The imbalance of RED (regional economic development) has always attracted people's attention. With the start of the western development and the shift of the national regional policy focus, our country's RD (regional development) has set off a climax. RED research has also entered a new stage. RED strategy and regional planning have become the focus of research. The main content of this paper is the application research of DMT in regional economic analysis. This article mainly uses a combination of quantitative and qualitative methods for experiments. First, the analysis of the current situation of the domestic regional economy is carried out. The results show that the east is still the strongest economic region in our country. The GDP of the east accounts for 49.7% of the country. The overall economic strength of the west is weaker than that of the east and central, but its economic development speed is at the leading level in the country, and the GDP growth rate has reached 20.5% over the previous year. Secondly, the main factors affecting the regional economy are labor force and natural resources. Finally, the application effect of DMT (data mining technology) in regional economic analysis is analyzed. The results showed that 74 people believed that data mining technology could optimize the development structure of the regional economy, 52 people suggested that data mining technology can affect the factors of regional economic development, and the remaining 43 and 31 people respectively believed that the use of data mining technology could weaken distance cost and Reduce information asymmetry.KeywordsData mining technologyCluster analysisRegional economyMacro analysis

Parkinson's disease (PD) is the second most common neurological disorder after Alzheimer's disease. Key clinical features of PD are motor-related and are typically assessed by healthcare providers based on qualitative visual inspection of a patient's movement/gait/posture. More advanced diagnostic techniques such as computed tomography scans that measure brain function, can be cost prohibitive and may expose patients to radiation and other harmful effects. To mitigate these challenges, and open a pathway to remote patient-physician assessment, the authors of this work propose a data mining–driven methodology that uses low cost, non-invasive sensors to model and predict the presence (or lack therefore) of PD movement abnormalities and model clinical subtypes. The study presented here evaluates the discriminative ability of non-invasive hardware and data mining algorithms to classify PD cases and controls. A 10-fold cross-validation approach is used to compare several data mining algorithms in order to determine that which provides the most consistent results when varying the subject gait data. Next, the predictive accuracy of the data mining model is quantified by testing it against unseen data captured from a test pool of subjects. The proposed methodology demonstrates the feasibility of using non-invasive, low cost, hardware and data mining models to monitor the progression of gait features outside of the traditional healthcare facility, which may ultimately lead to earlier diagnosis of emerging neurological diseases.

More and more literatures have researched the application of data mining technology in customer segmentation, and achieved sound effects. One of the key purposes of customer segmentation is customer retention. But the application of single data mining technology mentioned in previous literatures is unable to identify customer churn trend for adopting different actions on customer retention. This paper focus on constructs a integrated data mining and survival analysis model to segment customers into heterogeneous group by their survival probability (churn trend) and help enterprises adopting appropriate actions to retain profitable customers according to each segment’s churn trend. This model contains two components. Firstly, using data mining clustering arithmetic cluster customers into heterogeneous clusters according to their survival characters. Secondly, using survival analysis predicting each cluster’s survival/hazard function to identify their churn trend and test the validity of clustering for getting the correct customer segmentation. This model proposed by this paper was applied in a dataset from one biggest china telecommunications company. This paper also suggests some propositions for further research.KeywordsData MiningCustomer Relationship ManagementCustomer SegmentCustomer RetentionHeterogeneous ClusterThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.