Data Mining and Knowledge Engineering

M. Narmadha ¹, R. Kavitha ², K. Uma Maheshwari ³, K. Divya ⁴

Affiliations
1 Velammal College of Engineering and Technology, Madurai, IN
2 Department of Computer Science, Velammal College of Engineering and Technology, Madurai, IN
3 Sri Ramakrishna Engineering College, Coimbatore, IN
4 Hindustan University, Chennai, IN

Abstract

Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data.The difficulty in privacy-sensitive domain is solved by the development of the Multi-Level Trust Privacy Preserving Data Mining (MLT-PPDM) where multiple differently perturbed copies of the same data are available to data miners at different trusted levels. In MLT-PPDM data owners generate perturbed data by various techniques like Batch generation and On-demand generation. MLT-PPDM can overcome the diversity attacks. Partial information hiding methodologies like random perturbation, random rotation perturbation are incorporated with MLT-PPDM to enhance data security and to prevent leakage of the sensitive data. The solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on demand. Finally MLT-PPDM approach is improved to tackle against the non-linear attacks. The time and space complexities are calculated for both techniques and the results show that on-demand algorithm is best among them.

Keywords

Gaussian Noise, Multi-Level Trust, Partial Information Hiding, Perturbation Technique, Single Level Trust.

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R. Kavitha ¹

Affiliations
1 Department of MCA, Aloysius Institute of Management and Information Technology (AIMIT), Mangalore, Karnataka, IN

Abstract

E-learning plays a critical role in education. Each learner is having their own way of learning style that cannot be assessed in an exclusive way. They should not be evaluated only on number of right and wrong answers. Testing must be intelligent to pose intellectual questions based on the performance during the session. So, questions have to be classified based on the item difficulty using item responses. The study involves the categorization of questions based on ANN and ANFIS techniques. This paper reports the investigation of the effectiveness and performances of these methods to observe the question classification abilities depending on item responses, item difficulty and question levels. The effectiveness of these methods was evaluated by comparing the performance and class correctness. The comparative test performance analysis based on error rating revealed that ANFIS yield better performance. This study is focused because, each item affects a students' overall success throughout the test in terms of difficulty.

Keywords

Intellectual Question Classification, E-Learning, ANFIS.

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A. S. Kavitha ¹, R. Kavitha ¹

Affiliations
1 Department of Computer Science, P.S.G.R. Krishnammal College for Women, Coimbatore, IN

Abstract

Business enterprises invest lots of money to develop data warehouse that gives them real, constant and up to date data for decision making. To keep data warehouse update, traditionally, data warehouses are updated periodically. Periodic updates make a delay between operational data and warehouse data. These updates are triggered on time set; some may set it to evening time when there is no load of work on systems. This fixing of time does not work in every case. Many companies run day and night without any break, then in these situations periodic updates stale warehouse. This delay depends upon the periodic interval, as interval time increase the difference between operational and warehouse data also increase. The most recent data is unavailable for the analysis because it resides in operational data sources. For timely and effective decision making warehouse should be updated as soon as possible. Extraction, Transformation and Loading (ETL) are designed tools for the updating of warehouse. When warehouse is refreshed for the update purpose, it often gets stuck due to overloading on resources. Perfect time should be chosen for the updating of warehouse, so that utilize our resources can be utilized efficiently. Warehouse is not updated once, this is cyclic process. Here this paper is introducing automation for ETL, the proposed framework will select best time to complete the process, so that warehouse gets updated automatically as soon as resources are available without compromising on data warehouse usage.

Keywords

ETL, Updating, Loading, Data Warehouse.

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