scholarly journals Using Affinity Analysis-Driven Adaptive Data Mining Life Cycle for the Development of a Student Retention DSS

2021 ◽  
Vol 18 ◽  
pp. 135-147
Author(s):  
Pi-Sheng Deng
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Student Retention ◽  
Association Rule ◽  
Technological Development ◽  
Educational Institutions ◽  
Global Competition ◽  
Development Life Cycle ◽  
Three Phase ◽  
Rule Set

Technological development has engaged educational institutions in fierce global competition. To be competitive in meeting the changing needs of today’s student population, educational institutions find it imperative to prioritize student retention efforts and to develop strategies that interact and serve students more effectively in providing them more value and service. In this research we proposed a three-phase-six-stage adaptive data mining development life cycle, and we applied the affinity analysis to this methodology in identifying more than 400 association relationships with student retention, refining iteratively the association rule set down to less than 30 rules, and developing useful strategic implications regarding how the important factors were associated with a student’s decision. This set of implications and factors could then be integrated into the development of strategies for student retention

scholarly journals Data Transformation Techniques for Academic Datasets

2019 ◽  
Vol 9 (1) ◽  
pp. 2214-2218
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Data Preprocessing ◽  
Data Normalization ◽  
Multi Layer Perceptron ◽  
Data Preparation ◽  
Transformation Techniques ◽  
Development Life Cycle ◽  
Heterogeneous Datasets ◽  
Mining Model

Data mining is a real-world procedure of discovering useful patterns from heterogeneous datasets. All most all industry uses data mining in their day to day activities. To build an effective mining model, a series of development steps are to be followed. It starts with discovering the business problem and ends with communicating the results. In this development life cycle, the most important step is data preparation or data preprocessing. Data preprocessing is converting raw data into data understandable by the machine. Data normalization is a phase in data preprocessing where the data values are scaled to 0 and 1. Right normalization of the datasets leads to improved mining results. In this paper, academic data of students is taken. The dataset is normalization using six normalization technique. Multi Layer Perceptron classifier is applied to normalized dataset and results are obtained. Results of this study reveal the best normalization technique which can be used for normalizing academic datasets. Finally, in a line, the goal of this work is to discover the best normalization technique which produces better mining result when applied to academic datasets.

Applications of Data Mining in Software Development Life Cycle

2014 ◽  
pp. 67-79
Author(s):  
Naveen Dahiya ◽  
Vishal Bhatnagar ◽  
Manjeet Singh ◽  
Neeti Sangwan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Software Development ◽  
Future Research ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Development Life Cycle ◽  
Efficient Information ◽  
Software Development Life Cycle ◽  
Effective Use

Data mining has proven to be an important technique in terms of efficient information extraction, classification, clustering, and prediction of future trends from a database. The valuable properties of data mining have been put to use in many applications. One such application is Software Development Life Cycle (SDLC), where effective use of data mining techniques has been made by researchers. An exhaustive survey on application of data mining in SDLC has not been done in the past. In this chapter, the authors carry out an in-depth survey of existing literature focused towards application of data mining in SDLC and propose a framework that will classify the work done by various researchers in identification of prominent data mining techniques used in various phases of SDLC and pave the way for future research in the emerging area of data mining in SDLC.

scholarly journals Privacy preserving association rule hiding using border based approach

2021 ◽  
Vol 23 (2) ◽  
pp. 1137
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Sensitive Information ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Large Databases ◽  
Hidden Correlations ◽  
Rule Set

<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

MINING NON-REDUNDANT ASSOCIATION RULES BASED ON CONCISE BASES

2007 ◽  
Vol 21 (04) ◽  
pp. 659-675 ◽  
Author(s):  
YUE XU ◽  
YUEFENG LI
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Mining Community ◽  
Rule Set ◽  
Closed Itemsets ◽  
Minimal Generators

Association rule mining has many achievements in the area of knowledge discovery. However, the quality of the extracted association rules has not drawn adequate attention from researchers in data mining community. One big concern with the quality of association rule mining is the size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant, thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a reliable exact association rule basis from which more concise nonredundant rules can be extracted. We prove that the redundancy eliminated using the proposed reliable association rule basis does not reduce the belief to the extracted rules. Moreover, this paper proposes a level wise approach for efficiently extracting closed itemsets and minimal generators — a key issue in closure based association rule mining.

scholarly journals PERANCANGAN SISTEM INFORMASI PENCATATAN PEMBAYARAN SPP PADA PONDOK PESANTREN MODERN TGK CHIEK OEMAR DIYAN

2020 ◽  
Vol 5 (4) ◽  
pp. 560-570
Author(s):  
Ubbaidillah Ubbaidillah ◽  
Evayani Evayani
Keyword(s):  
Life Cycle ◽  
Information Systems ◽  
Programming Language ◽  
System Development ◽  
Boarding School ◽  
Educational Institutions ◽  
Development Life Cycle ◽  
Administrative Information

The development of technology today is very rapid and many innovations created such as administrative information systems have been used by many parties, both organizations and educational institutions. Especially in pesantren educational institutions that still use manual administrative information systems, therefore this study aims to assist the Administration in matters relating to the SPP payment process and SPP payment reports that are efficient and effective using desktops. This application is made with Xampp software and uses the PHP programming language that uses the System Development Life Cycle (SDLC) method. The results of the study were desktop-based SPP payment applications at the Tgk Chiek Oemar Diyan Modern Islamic Boarding School

Applications of Data Mining in Software Development Life Cycle

2016 ◽  
pp. 558-570
Author(s):  
Naveen Dahiya ◽  
Vishal Bhatnagar ◽  
Manjeet Singh ◽  
Neeti Sangwan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Software Development ◽  
Future Research ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Development Life Cycle ◽  
Efficient Information ◽  
Software Development Life Cycle ◽  
Effective Use

Data mining has proven to be an important technique in terms of efficient information extraction, classification, clustering, and prediction of future trends from a database. The valuable properties of data mining have been put to use in many applications. One such application is Software Development Life Cycle (SDLC), where effective use of data mining techniques has been made by researchers. An exhaustive survey on application of data mining in SDLC has not been done in the past. In this chapter, the authors carry out an in-depth survey of existing literature focused towards application of data mining in SDLC and propose a framework that will classify the work done by various researchers in identification of prominent data mining techniques used in various phases of SDLC and pave the way for future research in the emerging area of data mining in SDLC.

scholarly journals Rancang Bangun Aplikasi Data Mining pada Penjualan Distro Bloods Berbasis Web menggunakan Algoritma Apriori

2021 ◽  
Vol 4 (2) ◽  
pp. 26
Author(s):  
Muhammad Muttaqin Muchlis ◽  
Iskandar Fitri ◽  
Rini Nuraini
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Association Rule ◽  
Web Applications ◽  
A Priori ◽  
Sales Data ◽  
Development Life Cycle ◽  
Technological Developments ◽  
Rule Method ◽  
Purchasing Patterns

The design of this data mining application is a computerized system in the field of technology, this proves that technological developments in data processing are increasingly advanced, this can be the basis for the development of data processing systems for sales of bloods based web applications using a priori algorithms, problems in this bloods distribution cannot Minimizing the decline in sales at the Jakarta clothing event in 2019, it is necessary to evaluate the sales data, with market basket analysis or consumer shopping baskets to find out consumer shopping patterns as a reference for the sale strategy of event Jakarta clothing at the end of the year. This analysis uses a priori algorithm with the association rule method, while the SDLC (Software Development Life Cycle) method is used as the basis for developing expert systems. From the results of the study, it was found that sales data for 5 days and 7 items got the highest 100% confidence value from the itemset calculation 1,2,3 which passed the selection so that they became aware of consumer purchasing patterns and rearranged product layouts for promotion and improving the correct sales strategy.Keywords:Applications, Data Mining, Apriori Algorithms, Association Rule Method, SDLC.

scholarly journals An Intelligent Student Job Prediction using Educational Data Analytics

2020 ◽  
Vol 9 (5) ◽  
pp. 926-930
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Teaching And Learning ◽  
Data Science ◽  
Student Employment ◽  
Educational Institutions ◽  
Design Data ◽  
Student Management ◽  
Essential Knowledge

Mining of Educational data is an emerging field focused in data mining field to uncover required facts within educational data in order to assist educational institutions to increase their management design also student facilities. It provides essential knowledge about imparting the education, which is used to enhance the quality of teaching and learning. The implementation of the proposed system dataset provides details with respect to old students data. Mining of Educational data is implicated within data mining field to find the required facts inside educational data to assist institutions, to increase the management design along with learner facilities. The present study comes up with applying data science techniques over educational data. Association rule used within student’s data to find some facts for assisting management design. Data Science algorithms implemented by considering grade of courses, also graduated student employment information for job prediction after completion of education. The outcome of this study gives better knowledge for student management design and prediction of job. The main objective of the proposed system is to find the correlations between the student educational parameters with the types of the job.

scholarly journals Classification of Pruning Methodologies for Model Development using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 2043-2047 ◽  
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Model Development ◽  
Data Mining Techniques ◽  
Rule Set ◽  
Using Data ◽  
Rule Pruning ◽  
Class Association Rule

Knowledge discovery process deals with two essential data mining techniques, association and classification. Classification produces a set of large number of associative classification rules for a given observation. Pruning removes unnecessary class association rules without losing classification accuracy. These processes are very significant but at the same time very challenging. The experimental results and limitations of existing class association rules mining techniques have shown that there is a requirement to consider more pruning parameters so that the size of classifier can be further optimized. Here through this paper we are presenting a survey various strategies for class association rule pruning and study their effects that enables us to extract efficient compact and high confidence class association rule set and we have also proposed a pruning methodology..

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