Predictive data mining model for software bug estimation using average weighted similarity

2010 ◽  
Author(s):  
Naresh Kumar Nagwani ◽  
Shrish Verma
Keyword(s):  
Data Mining ◽  
Predictive Data Mining ◽  
Mining Model ◽  
Software Bug

scholarly journals Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence

2020 ◽  
Vol 11 (2) ◽  
pp. 1-10
Author(s):  
Bashar Shahir Ahmed ◽  
Mohamed Larabi Ben Maâti ◽  
Mohammed Al-Sarem
Keyword(s):  
Data Mining ◽  
Access Control ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Mining Algorithms ◽  
Control Functions ◽  
Customer Segment ◽  
Predictive Data Mining ◽  
Mining Model

The rising adoption of e-CRM strategies in marketing and customer relationship management has necessitated to more needs especially where a specific customer segment is targeted and the services are personalized. This paper presents a distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers. This hybrid model utilizes the integration of the mobile agent and client server technologies that could easily be updated from the already existing web platforms. The model allows the management team to derive insights from the operations of the system since it focuses on e-personalization and web intelligence hence presenting a better approach for decision support among organizations. To achieve this, a software approach made of access-control functions, data mining algorithms, customer-profiling capability, dynamic web page creation, and a rule-based system is utilized.

scholarly journals Enhancing the Quality Education using Predictive and Descriptive Data Mining Model

2019 ◽  
Vol 8 (3) ◽  
pp. 6843-6847
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Educational Data Mining ◽  
Strong Relationship ◽  
Data Mining Techniques ◽  
Huge Data ◽  
Student’S Performance ◽  
Predictive Data Mining ◽  
Mining Model ◽  
The Given

Data mining is the trending field used to get relevant knowledge from the database given. This technique consists of subfield called educational data mining is the emerging area used to extract the hidden patterns from the huge data with the help of tools techniques developed by the researchers of the educational data mining. The purpose of extracting patterns from the educational database is to improve the quality of education can be provided to the students for their better feature. The patterns are extracted by using the existing data mining techniques to enhance student performance. Educational data mining techniques such as classification, regression, clustering are available in the field. Classification is defined as the technique used to categorize the data based on the given label and constraints. In this paper, the algorithms like naves Bayes, Random Forest and J48 algorithms used to classify the data instances under the given labels using the constraints given., the classification algorithms like naves Bayes shows best performance accuracy with the given student dataset. Clustering and apriori rule have a strong relationship in student performance. In this paper, predictive data mining used to predict the student's performance to enhance the study level of the students in the organization.

Research on Key Technology of Data Mining for Volleyball Game Based on Service System

2014 ◽  
Vol 543-547 ◽  
pp. 4698-4701
Author(s):  
Juan Wang
Keyword(s):  
Data Mining ◽  
High Precision ◽  
Service System ◽  
Processing System ◽  
Machining Process ◽  
Precision Machining ◽  
Intelligent Processing ◽  
Mining Model ◽  
Processing Cycle ◽  
Key Parameter

During the processing of aircraft and other high precision machinery workpieces, if using the traditional machining methods, it will consume a amount of machining costs, and the mechanical processing cycle is long. In this context, this paper designs a kind of robot intelligent processing system with high precision machinery. And it has realized the intelligent online control on the machining process by using the high precision machining intelligent online monitoring technology and the numerical simulation prediction technology. Finally, this system is introduced into the process of data mining for volleyball game, and designs the partial differential variational data mining model, which has realized the key parameter data mining of volleyball games service system, and has provided reliable parameters and technical support for the training of volleyball players.

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