Data mining& warehouse oneshot all unit covered for rgpvexam 2024|7thsem|BTech
Data mining one shot units imp topic covered for rgpv exam 2024 |7thsem|b.tech#rgpvexam#youtube#exam
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comprehensive one-shot video
, we explore essential RGPV topics, starting with
Unit-5
clustering, its applications,
and real-world advantages.
Dive into association rule mining and its impact on recommendation systems.
Unit-4
Understand supervised learning with
classification,
statistical algorithms,
and Bayesian methods.
Learn about distance-based learning
and the significance of outliers in data analysis
. Explore index-based and rule-based learning approaches with practical examples.
Unit-2
unravel the concepts of OLAP operation
OLAP and OLTP, their applications, and key differences. Perfect for last-minute revision and gaining practical insights for exams and beyond
All unit covered
1) WHAT IS DATA MINING DISCUSS THE MAJOR ISSUES IN DETERMINING.
Or
Draw the architecture of determining system ?
2) Describe in brief various type of data on which mining can be performed (Imp)
3) what is data mining functionality explain different type of data mining functionality with example . (Imp)
4) what is data warehouse how it is different from operational database explain datamart.
5) discuss various schema to implement data warehouse.
6) differentiate between OLAP and OLTP
7) Explain stars to flakes and fact consteuation schema for multi-dimensional data model.
8) what are advantage of data cubes ? discuss the materialization of different kind of cubes .
9) Define association rule describe the concept of frequent item set, confidence and support.
10) Explain datadisation and concept hierarchy.
11) Describe in detail the Apriori algorithm list various way used to improve the efficiency of Apriori algorithm.
12) Explain HOLAP, MOLAP , ROLAP
13) How many type of clustering method are there ? explain any one partioning clustering algorithm
14) What is data transmission explain the different Methods of data transmission
15) what is classification rule ? describe major steps of decision tree classification.
16) write short note on
a) k-means flustering
b) cluster analysis
c) spatial data mining
d) Fp tree
e) Gini- index
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