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Saturday, October 27, 2012

Anna University Chennai/ Madurai /Trichy/Tirunelveli 2nd,4th,6th,8th Semester May/June 2012 revaluation results

Thursday, October 18, 2012

M.E/M.TECH LAB MANUAL / NOTES/PROGRAMS




M.E/M.TECH NETWORKS LAB MANUAL / NOTES/PROGRAMS





M.E/M.TECH DATA STRUCTURE LAB MANUAL / NOTES/PROGRAMS

M.E/M.TECH DATA STRUCTURE LAB PROGRAMS/MANUAL/NOTES




DATA STRUCTURE ME LAB PROGRAMS-->10. GRAPHCOLOURING



DATA STRUCTURE ME LAB PROGRAMS-->9. 0/1 KNAPSACK USING DYNAMIC PROGRAMMING


DATA STRUCTURE ME LAB PROGRAMS-->8. CONVEXHULL


DATA STRUCTURE ME LAB PROGRAMS-->7. QUICKSORT


DATA STRUCTURE ME LAB PROGRAMS--> 6. TRIES


DATA STRUCTURE ME LAB PROGRAMS-->5. B-TREE


DATA STRUCTURE ME LAB PROGRAMS-->4. AVL TREE


DATA STRUCTURE ME LAB PROGRAMS-->3.LEFTIST HEAP


DATA STRUCTURE ME LAB PROGRAMS-->2. DEAPS


DATA STRUCTURE ME LAB PROGRAMS-->1. MIN HEAP

M.E/M.TECH NETWORKS LAB MANUAL / NOTES



M.E/M.TECH NETWORKS LAB-->UDP SOCKETS


ME NETWORKS LAB-->TCP SOCKETS


ME NETWORKS LAB-->SIMULATION OF ROUTING PROTOCOL


ME NETWORKS LAB-->Simple network management protocol


ME NETWORKS LAB-->multi user chat


ME NETWORKS LAB-->FILE TRANSFER PROTOCOL


ME NETWORKS LAB-->DOMAIN NAME SERVER


ME NETWORKS LAB-->SIMULATION OF SLIDING WINDOW PROTOCOL

Friday, October 12, 2012

M.E./ DATA WAREHOUSING AND MINING NOTES


Definition of Data warehouse:


Data Warehouse Components:


Building a Data warehouse:


Data Integration and transformation

Classical Encryption

Mapping the data warehouse to a multiprocessor architecture

Database schema for decision support system:

Data Extraction, Transformation, and Migration Tools:

metadata and reporting

Query tools and implementation:

OLAP and multidimensional data analysis

Data Mining Functionalities:

Data Preprocessing-Data Cleaning

Data Integration and transformation

 Data reduction:

Data Discretization and Concept hierarchy Generation

Association Rule Mining


Efficient and scalable methods for mining frequent patterns

  Mining Various Kinds of Association Rules

Constraint based Association mining

Support Vector Machines

Associative Classification

Lazy Learners (or Learning from Your Neighbors)

Accuracy and Error Measures:

Evaluating the Accuracy of a classifier

Ensemble Learning and Model Selection

Cluster Analysis:

-Type of data in cluster analysis:

clustering methods

Hierarchical and partition based clustering


Density based and Grid based clustering


Model based Clustering:

Clustering high dimensional data

Constraint based cluster analysis

Outlier Analysis:   

Constraint-Based Clustering:

Spatial Data mining:

Outlier Analysis:   

Object data mining

Multimedia Data mining:

Text Data Mining:

 Web Data Mining

Multidimensional Data Analysis:

Descriptive mining of complex data objects

Clustering tools

- Weka tool implementation

R-software introduction

R-software execution for Model based clustering