Data Preprocessing Method For The Analysis Of Incomplete Data On Students In Poverty

The development of modern technology allows enterprises to continuously expand the means of financial management. The use of advanced information technology and modern management means makes the financial management of enterprises continue to improve, but there are also many problems. In the face of various types of risks that enterprises may face at present, in addition to strengthening financial accounting, enterprises should also recognize the various risks that the double-edged sword of financial management informatization may bring to enterprise financial management and carefully analyze the reasons for the emergence of risks, from the ideological and technical means to continuously improve the entire process of financial management informatization, which is an important topic for every financial worker to discuss. This paper mainly studies and innovates the data mining process and the support vector machine model and designs the data preprocessing method in the data mining process to perform feature selection and optimize the parameters of the support vector machine model. The specific process is as follows: based on CRISP-DM, the industry-standard model of the data mining process, a series of data preprocessing, eigenvalue extraction, parameter optimization, training set pruning, and other methods are designed from business understanding, data understanding, data preparation, and other aspects to improve data quality. In the traditional support vector machine, when the test sample is located at the boundary point of the hyperplane, the judgment may be wrong. In the aspect of SVM model improvement, according to the discrimination method of SVM, the weighted K-nearest neighbor algorithm is introduced to redistinguish the qualified test samples in the feature space. From the whole process of data mining, a data mining system is designed, which is “data preprocessing standardization + genetic algorithm feature selection + training set pruning + support vector machine classifier discrimination optimization.” Finally, this paper uses a series of data mining optimization methods designed to mine the actual financial data of listed companies and has achieved good results.

Data preparation is often cited as the most time consuming phase of a Knowledge Discovery and Data Mining (KDDM) process. This is attributed to the fact that this phase is highly dependent on the expertise of the analyst. Although process models exist for KDDM the description of their phases of the process focus on outlining what must be done but often do not detail how this should be done. While there is some research in addressing the how of the phases, the data preparation phase is thought to be the most challenging and is often described as an art rather than a science. The tasks defined in this phase are thought to be highly dependent on the expertise of the analyst and the context. While we are of the view that there will always be an art to data preparation we will demonstrate that the science can actually enhance the art. We further contend that as more research of this kind is published, that demonstrates a variety of data preparation techniques that enhance the data mining process, the more effective will be the science of data preparation.

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.

Pavement deflection data are often used to evaluate a pavement’s structural condition nondestructively. Pavement layers are important parameters in view of bearing capacity. Pavement layer thickness may be known from the design project or site investigation. At the same time, using backcalculation analysis, flexible pavement layer thicknesses together with in situ material properties can also be backcalculated from the measured field data through appropriate analysis techniques. Data mining (DM) process has not been used as a backcalculation tool before. In this study, DM process is used in backcalculating the pavement layer thickness from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the DM process in backcalculating the pavement layer thickness and compared each other. Performing the study, modeling with Kstar method gives fine results with respect to other DM modeling techniques. Backcalculation of pavement layer thickness using DM process has been carried out for the first time.

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

Analysing datasets using data mining techniques can enhance decision making in organizations. However, to ensure that the full potential of these techniques is realised it is important that decision makers understand there are Knowledge Discovery and Data Mining (KDDM) processes that are mature enough to be adopted. This paper demonstrates the benefits of using a KDDM process to evaluate survey data for internet banking users in Jamaica which includes demographic as well as attitudinal and behavioral variables. The major benefits of following this process include the selection of a set of models, rather than a single model, which are more relevant to the business/research objectives and use of a more targeted knowledge discovery process as the data mining analyst is now directed to consider the effects the decisions in each phase will have on subsequent phases. This leads to more relevant knowledge being extracted from the data mining process.

The data mining experiments from this chapter are a component of a larger research which is to be used to make several correlations, analysis in order to integrate data mining process in the managerial system for optimal decision support. I offered an insight of how data mining processes are being applied in the large spectrum of education by presenting recent applications and studies published in the scientific literature, considered to be relevant to the development of this emerging science. I presented my work through a number of experiments conducted over questionnaires data and scholastic databases at Faculty of Economics and Business Administration Cluj-Napoca, using classification learning and data clustering methods. In the last part of the chapter I introduced the concept of Academic Intelligence Management, and illustrated the integration of data mining processes and their particular role in higher education management and decision support. The studies will continue with deeper mining of educational topics, such as performance in scientific research, correlations between the students' knowledge and the competences demanded on the labor market, academic failure, to perceive what and how much the students know, to realize learning gaps, and also improve teaching methods and educational management processes.

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.

The majority of traditional finance management in colleges and universities is manual. This backward management mode brings a lot of inconvenience to financial processing. An essential concern in the daily financial administration of colleges and universities is how to efficiently gather, handle, and evaluate this important financial information and apply this beneficial knowledge to the daily management of colleges and universities. College and university finance administration has become increasingly complex due to the rapid growth of these institutions. The standard financial accounting management system is far from adequate for the daily needs of colleges and universities as their size grows. As a result, a new financial accounting management system for universities is being developed based on the combination of big data and blockchain data. The embedded processor, DDR2 memory chip, network interface, and USB interface are all designed by the hardware section. The software element examines the needs of university financial accounting management before designing a model based on big data and blockchain integration for university financial accounting management. Finally, the financial accounting management function module and database are designed and tested. The experimental results show that the designed financial accounting management system can effectively carry out financial management, has good performance, and has certain application value.

Data is identified as the fuel of modern society for its versatility of use and effectiveness of use. In addition, modern businesses are making a decline based on analysis of historical data and patterns of the data. Such dependency on data analysis makes the process of data analysis important for data mining. Therefore the overall study has shed light on the significance of the data mining process and extraction process of data in order to make a data-driven decision. Additionally, the problems related to the process of data extraction and data mining are mentioned in the study which helps to achieve an overall concept for the data extraction and data mining process. Additionally, the significance of the process is mentioned in the study. Additionally, there are tables constructed that represent problems of the data extraction process and mining and the significance of the stems of mining. The study concludes in a way that helps in the implication of data extraction methods for business.

The knowledge discovery and data mining (KDDM) process models describe the various phases (e.g. business understanding, data understanding, data preparation, modeling, evaluation and deployment) of the KDDM process. They act as a roadmap for implementation of the KDDM process by presenting a list of tasks for executing the various phases. The checklist approach of describing the tasks is not adequately supported by appropriate tools, which specify ‘how’ the particular task can be implemented. This may result in tasks not being implemented. Another disadvantage is that the long checklist does not capture or leverage the dependencies that exist among the various tasks of the same and different phases. This not only makes the process cumbersome to implement, but also hinders possibilities for semi-automation of certain tasks. Given that each task in the process model serves an important goal and even affects the execution of related tasks due to the dependencies, these limitations are likely to negatively affect the efficiency and effectiveness of KDDM projects. This paper proposes an improved KDDM process model that overcomes these shortcomings by prescribing tools for supporting each task as well as identifying and leveraging dependencies among tasks for semi-automation of tasks, wherever possible.

Prediction is one of the methods in data mining. One of the models that can be used in prediction is using linear regression. Linear regression is used to make predictions on the data that has been provided. In this study, a linear regression model was made with a datasheet containing data that affected student achievement in achieving final exam scores. The linear regression model developed can be used to predict student test scores. The linear regression model developed can be used to predict student test scores. The datasheet used in the test uses a public datasheet, namely student_performance.csv. The datasheet consists of 395 records and 33 attributes. The attributes used are selected that influence the label. The selection of attributes is based on the results of the weighting in the process of checking the correlation matrix. Based on the weighting, the attributes used are seven attributes and one attribute becomes a label. The research method uses CRISP DM which consists of business understanding, data understanding, data preparation, model making, evaluation, and deploying. The data mining process uses the Rapid Miner application. The results of the study resulted in a linear regression model y=0.729-(0.024×Medu)-(0.020×Fedu)+(0.053×failures)-(0.077×goout)-(0.012×absences)+(0.126×G1)+(0.862×G2). The result of evaluating the performance of the RMSE value was 0.675. Based on these results, it can be concluded that the resulting model can be recommended for use in predicting student test scores.

Data preparation is the most crucial stage before any machine learning algorithm can be applied. Equally important is text data preparation for many areas such as information retrieval, data mining, information extraction and natural language processing. In this paper, how text data preparation for short-text automated assessment grading is implemented in RapidMiner will be discovered. RapidMiner has becoming a very popular tool in field of data exploration, data preparation, modelling, scoring, automation and many other data science related. It is found that RapidMiner graphical visual environment makes it convenient, simple and fast to design a better model for automated grading. With this, an experiment is carried out to explore this option on short-text automated grading. The popularity of studying automated grading in teaching and learning has also becoming a popular area ever since it was introduced. The main challenge in studying this area is to extract important information out of a collection of unstructured text-based data. Thus, this paper will describe in detail how text data can be prepared in RapidMiner tool in order to discover relevant information for machine learning implementation.

Poor Student Assistance is very helpful for students to meet the needs in learning activities and in completing the students themselves is very supportive in government programs that have to study nine years (preferably up to the upper secondary level), to help smooth the Poor Student Assistance which is directly to students and this does not aim for students who excel because it considers the condition of students. To find out the pattern of determining poor students' assistance recipients can be done by utilizing student data and poor student aid recipient data. Searching for patterns of determining students who receive aid from poor students is expected to be able to analyze the factors that greatly affect the recipients of poor student assistance. The method in accordance with the above problems using the Description Method, Description is one of the uses of data mining to explore and collect a lot of data. The description method is useful for characterizing or describing a condition, history, object.Keywords: Data Mining, Poor Student Assistance, Method Description

Financial management, as the central link in the enterprise management chain, has emerged as a critical component in achieving the company’s strategic objectives. Businesses are transitioning from a closed to an open and scalable development model to an intensive development model, which is reflected in financial management and necessitates a growth-oriented transformation. Unreliability abounds in today’s globalized environment, from financial management-oriented accounting to efficiency accounting. In addition to general supply and demand unreliability, the high degree of complexity and increased channel unreliability make companies resist the great challenge of financial management systems’ ability to face uncertain events, corporate supply chains become increasingly vulnerable, and unreliability has become a common and indispensable phenomenon in many economic and management decision problems. Due to their high data requirements and dynamic adaptability, Markov models are used as the primary forecasting tool. They do not require a large amount of historical data, but rather a detailed analysis of the recent past. This thesis examines and resolves the issues with the current financial management system through a detailed examination of the enterprise financial management system’s reliability optimization, as well as identifying and supplying the appropriate financial system management tools for the company’s business activities. New business concepts and their advantages to management are introduced. The experimental results show that adding precision constraints to the floating-point calculation problem transforms it into an integer calculation problem, improving reliability by 0.389, demonstrating that the Markov model improves the efficiency of financial resource allocation. At the decision-making level, it provides a management platform and operational tools. As a result, research on this topic will assist businesses in developing a comprehensive business process that meets their needs and adapts to future development needs at a low cost, as well as assisting businesses in communicating with employees, external collaboration, and collaborative business between customers and partners.