Research on Application of Visualized Data Mining Technology

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.

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.

With the gradual popularization of smart devices and the prosperity of Internet technology, the domestic automobile industry is also undergoing a shift from traditional power to new energy. Although the existing new energy technology has been widely used in the automotive field, there is still a certain gap between our country's overall new energy technology innovation capability and developed countries. Finding a business model suitable for the development of new energy vehicles has become a key factor in promoting the development of the new energy vehicle industry. Therefore, this article mainly discusses the path research of data mining analysis technology in the business model innovation of new energy vehicles. First, list the current status of the existing traditional business models of 100 new energy automobile companies, and then analyze the car sales data of the A company in the 100 companies in the past five years. The total annual car sales of A company have gradually increased, from 5512 in 2017 to 9070 in 2020. In just four years, the sales of cars have increased by nearly 3500. To a certain extent, data mining technology provides decision-making support for the continuous innovation of business models of new energy automobile enterprises and brings more automobile sales to enterprises.KeywordsData miningNew energy vehiclesBusiness modelsCluster analysis

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.

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.

The paper introduces a "lazy" Data Mining technology, which models students' learning characteristics by considering real data instead of deriving ("guessing") their characteristics explicitly. In former work, the authors developed a modeling system for university learning processes, which aims at evaluating and refining university curricula to reach an optimal learning success in terms of the best possible grade point average (GPA). This is performed by applying an Educational Data Mining (EDM) technology to former students curricula and their degree of success (GPA) and thus, uncovering golden didactic knowledge for successful education. Here, we introduce a complementary technology to mine course characteristics similarities of former students' study traces and utilize them to optimize curricula of current students based to their performance traits revealed by their study achievements so far. This way, technology generates suggestions of personalized curricula. Furthermore, this technology is supplemented by an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.).

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.

Data mining is a useful technology that can support new discoveries based on the pattern analysis and a variety of linkages between data, and currently is utilized in various fields such as finance, marketing, medical. In this paper, we propose an effective utilization method of data based on analysis of spatial data mining. We make use of basic data of foreigners living in Seoul. However, the data has some features distinguished from other areas of data, classification as sensitive information and legal problem such as personal information protection. So, we use the basic statistical data that does not contain personal information. The main features and contributions of the proposed method are as follows. First, we can use Big Data as information through a variety of ways and can classify and cluster Big Data through refinement. Second. we can use these kinds of information for decision-making of future and new patterns. In the performance evaluation, we will use visual approach through graph of themes. The results of performance evaluation show that the analysis using data mining technology can support new discoveries of patterns and results.

With the rapid development of the logistics industry, the data and information of logistics are also growing geometrically. However, the current information system is usually unable to deeply analyze these huge data and information, so as to accurately understand the law of the development of the industry and the direction that should be guided . Therefore, it is necessary to establish a new information system. The traditional database management information system can not make good use of and analyze the large amount of data accumulated in the database. Data mining and data warehouse technology can solve this problem. Data mining is a technology dedicated to data analysis and understanding, revealing the knowledge contained in the data. It is one of the important goals of the application of information technology in the future. It can explore and analyze a large number of data, mining meaningful rules, in order to provide appropriate reference and suggestions for future decision-making. In recent years, data mining technology has been widely studied and applied, and has been successfully applied in the fields of Commerce, finance, medical treatment and so on.KeywordsData miningLogistics transportationMultimodal transportationGenetic algorithmDynamic programming

Today is the world of knowledge economy. The business unit which believes in creating new innovations and implementing this technology in their organization as it always provides an edge over traditional working. Patents are used for preserving this knowledge. A large number of patents are filed every day. Identifying the best patent as per business need and extracting relevant knowledge from patients will help organizations to enhance their business. There are many technologies of data mining that can be suitably used for this purpose. This paper compares the key features of different techniques used for mining hidden patterns from patents. Today is the world of knowledge economy. The business unit which believes in creating new innovations and implementing this technology in their organization as it always provides an edge over traditional working. Patents are used for preserving this knowledge. A large number of patents are filed every day. Identifying the best patent as per business need and extracting relevant knowledge from patients will help organizations to enhance their business. There are many technologies of data mining that can be suitably used for this purpose. This paper compares the key features of different techniques used for mining hidden patterns from patents. Today is the world of knowledge economy. The business unit which believes in creating new innovations and implementing this technology in their organization as it always provides an edge over traditional working. Patents are used for preserving this knowledge. A large number of patents are filed every day. Identifying the best patent as per business need and extracting relevant knowledge from patients will help organizations to enhance their business. There are many technologies of data mining that can be suitably used for this purpose. This paper compares the key features of different techniques used for mining hidden patterns from patents.

Executive decisions are the core components affecting the growth of the organization. While one right decision can make the business to reach the sky, one wrong decision can bring down the business. With the increasing competition of IT industries, the investments, business directives and the business data are increasing exponentially. Hence, the business owner should be extra cautious and must keep all the factors in mind while decision making. Thus, demanding a great requirement of a tool which measures and monitors the growth of the business and to evidence that the business is heading towards the profitable direction. Even though there are few existing methods to measure the growth of the company, these are limited to provide basic information or have restrictions in analyzing the behavior of the business. Therefore, fail to provide the complete assurance to business owners in decision making. This work provides an efficient Nobel solution to address these problems by focusing on developing a Performance Dashboard. The proposed technique involves an integration of business intelligence technologies, data mining and data visualization technologies creating a perfect solution to analyze the business trends, business growth, the amount of profit, employee performance, customer satisfaction, areas of improvements in business and much more. This performance dashboard showcases the information by understating the business behavior right from the organization start period. It acts as an information management tool that is used to track the metrics, Key Performance Indicators (KPIs) and additional key factors applicable to the business or specific process. Using data visualization techniques, dashboard simplifies the complex data sets to deliver users with a glancing awareness of present performance and to keep track on the department's capability to accomplish service level targets.

With the development of the technology of artificial intelligence, statistical analysis and database technology of data mining gradually, many data mining methods are applied to the field of poultry disease research. This paper study the strategy pattern of object oriented software design patterns in data mining application scientific research software platform design and development, and puts forward the design summary of the platform. The paper proposes a data mining study ways of thinking: in order to achieve the disease data encapsulation algorithm package, to achieve a unified interface. Realize different data mining methods in a certain type of disease problems, A certain kind of data mining methods used in different diseases.Keywordsdata miningpoultry diseasestatistical analysis

With the explosion of information and ever-increasing information resources, humanity has entered a brand-new cloud computing era. We are confronted with a brand-new problem: how to quickly and accurately extract the required information from vast information resources. It is even possible to argue that university educational administration is inextricably linked to university teaching accomplishments. The teaching data mining (DM) technology was created in order to extract the required information from vast information resources. People’s ability to find data using cloud computing technology (IT) has improved. This paper analyzes the division and optimal allocation of talent types in universities, in combination with the development strategy and HR characteristics of universities, and provides strategies for the rational allocation of talents in universities, as well as support for the information relationship of HR in universities, using DM technology research. The emergence of DM technology alleviates the problem of data accumulation-induced information explosion. In today’s universities, there are an increasing number of talents. In terms of talent allocation, the information-based talent allocation scheme offers new approaches to talent acquisition and university education development in the modern era.

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.