Application of classification technique of data mining for employee management system

This paper presents the application of classification technique of data mining used for the Employee Management System (EMS). This paper discusses the classification techniques of data mining and based on the data, the process of Knowledge Discovery in Databases (KDD) is reformed for classifying large data into different categories such as Disability, Employee Performance, etc. This paper discusses, WEKA data mining toolkit classifier model to predict employee’s performance based on the employee’s age, date of joining and number of years of experience. This study helps to predict the employee’s work-cycle and helps the management to find the employee’s performance those who are disabled and enabled. The paper addresses the system to get the details of those employees who need special attention and guide the management to make policies to improve employees’ performance. We demonstrate the application in a real-life situation. © Springer International Publishing AG, part of Springer Nature 2018

Database performance tuning and query optimization

Today, IT professionals are challenged with the task of ongoing improvements to achieve goals of businesses. Unfortunately, some factor/Resources, skill environment does not dynamically grow as fast as business needs. That sequence of events creates major obstacles for DB infrastructure, deployment, administration and maintenance. This paper discusses the performance issues, different bottlenecks such as CPU bottlenecks, Memory structures, Input output capacity issue, Database Design issues and Indexing issues. Also this paper address Tuning stages and how SQL queries can be optimized for better performance. In this paper we are focusing on query tuning tips & tricks which can be applied to gain immediate performance gain by creating Query Execution Flow Chart. We demonstrate the application of this technique in an Employee Biometric Attendance Management System

Prediction rules in e-learning systems using genetic programming

This paper describes the use of Data Mining Techniques to improve teaching–learning processes in the linear programming course offered at the Engineering Faculty at Mumbai University, India. The proposed approach seeks to model the student’s interaction with the study material using prediction rules whose interpretation will allow to detect the weaknesses of the educational process and evaluate the quality of the study material. The proposed rule discovery method is the Evolutionary Algorithms and particularly the Grammar-Based Genetic Programming (GB-GP), which is compared to association rules and decision tree construction for discovering prediction rules

Association rule mining for customer segmentation in the SMEs sector using the apriori algorithm

Customer´s segmentation is used as a marketing differentiation tool which allows organizations to understand their customers and build differentiated strategies. This research focuses on a database from the SMEs sector in Colombia, the CRISP-DM methodology was applied for the Data Mining process. The analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and the following grouping algorithms were applied on this model: k -means, k-medoids, and Self-Organizing Maps (SOM). For validating the result of the grouping algorithms and selecting the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers, so determining association according to loyalty

Data mining and neural networks to determine the financial market prediction

Predicting stock market movements has been a complex task for years by gaining the increasing interest of researchers and investors present all around the world. These have tried to get ahead of the way in order to know the levels of return and thus reduce the risk they face in investments [1]. Capital markets are areas of fundamental importance for the development of economies and their good management that favors the transition from savings to investment through the purchase and sale of shares [2]. These actions are so important that they are influenced by economic, social, political, and cultural variables. Therefore, it is reasonable to consider the value of an action in an instant not as a deterministic variable but as a random variable, considering its temporal trajectory as a stochastic process

Determination of contents based on learning styles through artificial intelligence

The study presents the development of a platform for structuring adaptive courses based on active, reflexive, theoretical and pragmatic learning styles using artificial intelligence techniques. To this end, the following phases were followed: search, analysis and classification of information about the process of generating content for courses; analysis and coding of the software component for generating content according to learning styles; and application of tests for validation and acceptance. The main contribution of the paper is the development of a model using neural networks and its integration in an application server to determine the contents that correspond to the active, reflexive, theoretical and pragmatic learning styles