Data Mining

                                                                                                                                             
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                                        Domain Description

            Data mining is the use of automated data analysis techniques to uncover previously undetected relationships among data items. Data mining often involves the analysis of data stored in a data warehouse. Three of the major data mining techniques are regression, classification and clustering.

                                      

1. IDENTIFYING INTERESTING VISITORS THROUGH WEB LOG CLASSIFICATION (IEEE)

                                                                                                   View Abstract

                Web mining is a popular technique for analyzing visitor activities in e-service systems. The ultimate goal of Web mining is to identify actionable information that can help, for example, acquire new customers, retain old customers, and grow customers’ profitability.

System Requirement Specification:-
DOMAIN       : TRANSACTION ON DATA MINING
SOFTWARE  : Operating System: Windows xp, Platform: JAVA,
HARDWARE  : Processor:  Pentium-IV,  Speed: 1.8 GHZ , RAM: 512 MB,  HDD: 80 GB

2. WATERMARKING RELATIONAL DATABASES USING OPTIMIZATION BASED 
    TECHNIQUES (IEEE-2008)

                                                                                                    View Abstract

           Proving ownership rights on outsourced relational databases is a crucial issue in today internet-based application environment and in many content distribution applications. In this paper, we present a mechanism for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. We formulate the watermarking of relational databases as a constrained optimization problem, and discuss efficient techniques to solve the optimization problem and to handle the constraints. Our watermarking technique is resilient to watermark synchronization errors because it uses a partitioning approach that does not require marker tuples. Our approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. We implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration and insertion attacks.

System Requirement Specification:-

DOMAIN     : TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
SOFTWARE : Operating System:WindowsXP, Platform:DOTNET, BackEnd :SQL SERVER,
                  Protocol : UDP
HARDWARE :  Processor:  Pentium-IV ,  Speed: 1.8 GHZ , RAM: 512 MB,  HDD: 80 GB

3. IDENTIFYING INTERESTING VISITORS THROUGH WEB LOG CLASSIFICATION (IEEE)

                                                                                                    View Abstract

                Web mining is a popular technique for analyzing visitor activities in e-service systems. The ultimate goal of Web mining is to identify actionable information that can help, for example, acquire new customers, retain old customers, and grow customers profitability.

System Requirement Specification:-
DOMAIN     : TRANSACTION ON DATA MINING
SOFTWARE : Operating System: Windows xp, Platform: JAVA, Back end:Ms-access
HARDWARE : Processor: Pentium-IV, Speed: 1.8 GHZ , RAM: 512 MB, HDD: 80 GB

4.  Near-uniform Range Partition Approach for Increased Partitioning

                                                                                                    View Abstract

                  Database partitioning technique which adopts divide and conquer method can efficiently simplify the complexity of managing massive data and improve the performance of the system, especially the range partitioning. The traditional range partitioning approach brings heavy burden to the system without an increased partitioning algorithm, so it does not adapt to the partitioning in the realtime data environment. To speed up the partitioning algorithm, the current partitioning technology is well studied and three effective range partitioning algorithms for the massive data are proposed, which based on allowing the fluctuation of data amount in each range of partitions. At last, some experiments and applications show that the proposed algorithms are more effective and efficient to partitioning and re-partitioning tables in the large database or real-time environment.

System Requirement Specification:-

DOMAIN     : Data Mining
SOFTWARE : Operating System: Windows XP , Platform: DOTNET
HARDWARE : Processor:Pentium-IV, Speed: 1.8 GHZ , RAM: 512 MB, HDD: 80 GB

                                                       
 
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