BE/ME/B.TECH/M.TECH ENGINEERING & LECTURER NOTES & QUESTION PAPERS, GENERAL TOPICS,INTERVIEW QUESTIONS,APTITUDE PAPERS,MODEL PAPERS,PLACEMENT PAPERS, EXAM RESULTS,ANNA UNIVERSITY REVALUATION RESULTS 2012 & MANY MORE....
Saturday, October 27, 2012
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
Thursday, October 11, 2012
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