Data Mining Applications in Higher Education and Academic Intelligence Management

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.

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.

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.

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.

PurposeThe purpose of this study is to assess students' behaviors toward biodiversity management in higher education institutions. This study expands upon the value belief norm (VBN) theory by examining how environmental values, beliefs and norms impact students' engagement in biodiversity management.Design/methodology/approachAn online survey was undertaken using a Google questionnaire. Convenience and snowball sampling techniques were adopted to reach the targeted students. The proposed associations were examined through the application of SEM with the use of AMOS software.FindingsThe findings provide valuable insights into students' behaviors regarding biodiversity management and demonstrate that extending VBN theory to the context of biodiversity, with the incorporation of environmental knowledge and environmental-friendly activities, resulted in a robust model for understanding students' behaviors toward biodiversity management in higher education institutions.Research limitations/implicationsThe findings can guide targeted interventions to boost student engagement in biodiversity management. Understanding the factors influencing their behaviors can aid in designing effective educational programs that promote biodiversity conservation and management.Originality/valueThis uniqueness of this study rests in its extension of the VBN theory to the context of biodiversity management, the exploration of environmental knowledge and environmental-friendly activities, and its focus on higher education institutions. Its value lies in guiding targeted interventions and campus sustainability strategies, aligning with UN SDGs to support global biodiversity and sustainable practices.

Data mining (DM) is a powerful information technology (IT) tool in today’s competitive business world, especially as our human society entered the Big Data era. From academic point of view, it is an area of the intersection of human intervention, machine learning, mathematical modeling and databases. In recent years, data mining applications have become an important business strategy for most companies that want to attract new customers and retain existing ones. Using mathematical techniques, such as, neural networks, decision trees, mathematical programming, fuzzy logic and statistics, data mining software can help the company discover previously unknown, valid, and actionable information from various and large sources (either databases or open data sources like internet) for crucial business decisions. The algorithms of the mathematical models are implemented through some sort of computer languages, such as C++, JAVA, structured query language (SQL), on-line analysis processing (OLAP) and R. The process of data mining can be categorized as selecting, transforming, mining, and interpreting data. The ultimate goal of doing data mining is to find knowledge from data to support user’s decision. Therefore, data mining is strongly related with knowledge and knowledge management.

Many organisations across a variety of industries are engaging in the process of data mining as part of an overall strategy for business intelligence, customer relationship management (CRM), including churn prevention. This paper provides an overview of the data mining process and illustrates a case study in which data mining is utilised as a churn prevention tool for a major Midwest USA newspaper. For this case study, a decision tree, a common modelling technique, was the analytical tool of choice. Lessons learned throughout the data mining process are provided to offer insight and to promote the sharing of information. Strategies for getting started in the data mining process are presented to encourage organisations to embrace a data-driven strategy for business intelligence, CRM and churn prevention.

The data mining process is the sequence of tasks applied to data, in order to discover relations between them to have knowledge. However, the data mining process lacks a formal specification that allows it to be modelled independently of platforms. Model driven architecture (MDA) is an approach for the development of software systems, based on the use of models to improve their productivity. Several research works have been elaborated to align the MDA approach with data mining on data warehouses, to specify the data mining process in a very high level of abstraction. In our work, we propose a support architecture that allows positioning these researches in different abstraction levels, on the basis of several criteria; with the aim to identify strengths for each level, in term of modelling; and to have a clear visibility on the MDA contribution for data mining.

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.

The Problem: Standardized processes are often followed to systematically conduct data mining projects. However while current models provide good descriptions, they are in need of updates given current Big Data challenges. Current data mining methods do not meet all requirements of businesses, in addition current methods are difficult to remember and do not cover all requisite steps. Given these limitations, usage of the traditional data mining process methods are fading in favor of independent data mining processes. What Was Done: BEMO Business Opportunity, Exploration, Modeling, and Operationalization is a standard parsimonious process developed for conducting data mining projects in a reusable and repeatable fashion in a Big Data environment. This model is vendor, technology, and industry agnostic. The process model is applied to a practical project example. Why this Work is Important: This manuscript allows a reusable and simplified model for data mining that can be applied to a variety of applications given a formalized and detailed process template. Given new technologies, Big Data and other developments a new data mining methodology is required to adequately meet these needs. The contribution of a parsimonious Big Data mining model also permits utilizing simpler models over complex models that can more efficiently generalize new problems.

The increasing awareness of data mining technology, along with the attendant increase in the capturing, warehousing, and utilization of historical data to support evidence-based decision making, is leading many organizations to recognize that the effective use of data is the key element in the next generation of client-server enterprise information technology. The concept of data mining is gaining acceptance in business as a means of seeking higher profits and lower costs. To deploy data mining projects successfully, organizations need to know the key factors for successful data mining. Implementing emerging information systems (IS) can be risky if the critical success factors (CSFs) have been researched insufficiently or documented inadequately. While numerous studies have listed the advantages and described the data mining process, there is little research on the success factors of data mining. This dissertation identifies CSFs in data mining projects. Chapter 1 introduces the history of the data mining process and states the problems, purposes, and significances of this dissertation. Chapter 2 reviews the literature, discusses general concepts of data mining and data mining project contexts, and reviews general concepts of CSF methodologies. It also describes the identification process for the various CSFs used to develop the research framework. Chapter 3 describes the research framework and methodology, detailing how the CSFs were identified and validated from more than 1,300 articles published on data mining and related topics. The validated CSFs, organized into a research framework using 7 factors, generate the research questions and hypotheses. Chapter 4 presents analysis and results, along with the chain of evidence for each research question, the quantitative instrument and survey results. In addition, it discusses how the data were collected and analyzed to answer the research questions. Chapter 5 concludes with a summary of the findings, describing assumptions and limitations and suggesting future research.

Medical data mining and knowledge discovery can benefit from the experience and knowledge of clinicians, however, the implementation of this data mining system is challenging. Unlike traditional data mining methods, in this class of applications we process data with some posterior knowledge and the target function is more complex and even may include the opinion of user. Despite the success of the classical reasoning algorithms in many common data mining applications, they failed to address medical record processing where we need to extract information from incomplete, small samples along with an external rulebase to generate ‘meaningful’ interpretation of biological phenomenon. Swarm intelligence is an alternative class of flexible approaches that is promising in data mining. With full control over the rule extraction target function, particle swarm optimization (PSO) is a suitable approach for data mining subject to a rulebase which defines the quality of rules and constancy with previous observations. In this chapter we describe a complex clinical problem that has been addressed using PSO data mining. A large group of temporal lobe epilepsy patients are studied to find the best surgery candidates. Since there are many parameters involved in the decision process, the problem is not tractable from traditional data mining point of view, while the new approach that uses the field knowledge could extract valuable information. The proposed method allows expert to interaction with data mining process by offering manual manipulation of generated rules. The algorithm adjusts the rule set with regard to manipulations. Each rule has a reasoning which is based on the provided rulebase and similar observed cases. Support vector machine (SVM) classifier and swarm data miner are integrated to handle joint processing of raw data and rules. This approach is used to establish the limits of observations and build decision boundaries based on these critical observations.

The advancement of technology has greatly promoted education. And with the popularization of information technology construction in higher education institutions, the concept of "Big Data" has been paid more and more attention in the management of higher education. Data mining is used to analyze students learning situation. The design of the academic early warning management system can effectively prevent some students from being at risk of dropping out of school due to academic difficulties, improve the quality of higher education operations, and provide higher education managers with the idea of carrying out targeted risk prevention.

This article gives a brief overview of various aspects of data mining of multispectral image data. We focus on specifically the remote sensing satellite images acquired using multispectral imaging (MSI), given the technology used across multiple knowledge domains, such as chemistry, medical imaging, remote sensing, and so on with a sufficient amount of variation. In this article, the different data mining processes are reviewed along with state‐of‐the‐art methods and applications. To study data mining, it is important to know how the data are acquired and preprocessed. Hence, those topics are briefly covered in the article. The article concludes with applications demonstrating the knowledge discovery from data mining, modern challenges, and promising future directions for MSI data mining research.This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Big Data Mining