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Data Warehousing and Data Mining - Association Rule Mining and Classification - Important Short Questions and Answers : Association Rule Mining and Classification

**What is Association rule?**

Association
rule finds interesting association or correlation relationships among a large
set of data items which is used for decision-making processes. Association
rules analyzes buying patterns that are frequently associated or purchased
together.

**What are the Applications of
Association rule mining?**

Basket
data analysis, cross-marketing, catalog design, loss-leader analysis,
clustering, classification, etc.

**Define support and confidence in
Association rule mining.**

Support S
is the percentage of transactions in D that contain AUB. Confidence c is the
percentage of transactions in D containing A that also contain B. Support (
A=>B)= P(AUB)

Confidence
(A=>B)=P(B/A)

**How are association rules mined
from large databases? **

Association
rule mining is a two-step process.

Find all frequent itemsets.

Generate strong association rules from the frequent
itemsets.

.

**Define Data Classification.**

It is a
two-step process. In the first step, a model is built describing a
pre-determined set of data classes or concepts. The model is constructed by analyzing
database tuples described by attributes. In the second step the model is used
for classification.

**Describe the two common
approaches to tree pruning.**

In the
prepruning approach, a tree is ―pruned‖ by halting its construction early. The
second approach, postpruning, removes branches from a ―fully grown‖ tree. A
tree node is pruned by removing its branches.

**What is a ―decision tree‖?**

It is a
flow-chart like tree structure, where each internal node denotes a test on an
attribute, each branch represents an outcome of the test, and leaf nodes
represent classes or class distributions. Decision tree is a predictive model.
Each branch of the tree is a classification question and leaves of the tree are
partition of the dataset with their classification.

**What do you meant by concept
hierarchies?**

A concept
hierarchy defines a sequence of mappings from a set of low-level concepts to
higher-level, more general concepts. Concept hierarchies allow specialization,
or drilling down ,where by concept values are replaced by lower-level concepts

**Where are decision trees mainly
used?**

Used for
exploration of dataset and business problems Data preprocessing for other predictive
analysis Statisticians use decision trees for exploratory analysis

**What is decision tree pruning?**

Once tree
is constructed , some modification to the tree might be needed to improve the
performance of the tree during classification phase. The pruning phase might
remove redundant comparisons or remove subtrees to achieve better performance.

**Explain ID3**

ID3 is
algorithm used to build decision tree. The following steps are followed to
built a decision tree.

Chooses splitting attribute with highest information
gain.

Split should reduce the amount of information
needed by large amount.

**What is
Classification?**

predicts categorical class labels

classifies data (constructs a model) based on the
training set and the values (class labels) in a classifying attribute and uses
it in classifying new data

**What is Prediction?**

models continuous-valued functions, i.e., predicts
unknown or missing values

**What is supervised learning
(classification)**

Supervision: The training data (observations,
measurements, etc.) are accompanied by labels indicating the class of the
observations

New data is classified based on the training set

**What is
Unsupervised learning (clustering)**

The class labels of training data is unknown

Given a set of measurements, observations, etc.
with the aim of establishing the existence of classes or clusters in the data

**16.Define Decision tree**

A flow-chart-like tree structure

Internal
node denotes a test on an attribute o Branch represents an outcome of the test

Leaf nodes represent class labels or class
distribution

**17.What is the Use of decision
tree? Classifying an unknown sample**

Test the attribute values of the sample against the
decision tree

**18.What are the other
classification methods? **

k-nearest
neighbor classifier

Rough set
approach

case-based
reasoning

Fuzzy set
approaches

Genetic
algorithm

**19. What is linear regression?**

In linear
regression data are modeled using a straight line. Linear regression is the
simplest form of regression. Bivariate linear regression models a random
variable Y called response variable as a linear function of another random
variable X, called a predictor variable. Y = a + b X

**20. What is the classification of
association rules based on various criteria?**

Based on the types of values handled in the rule.

Boolean Association rule.

Quantitative Association rule.

Based on the dimensions of data involved in the
rule.

Single Dimensional Association rule.

Multi Dimensional Association rule.

Based on the levels of abstractions involved in the
rule.

Single level Association rule.

Multi level Association rule.

Based on various extensions to association mining.

Maxpatterns.

Frequent closed item sets.

**21.What is the purpose of Apriori
algorithm?**

The name
of the algorithm is based on the fact that the algorithm uses *prior knowledge* for find frequent

Item set

**22.What are the two steps of Apriori algorithm?**

Join step

Prune step

**23.Give the few techniques to
improve the efficiency of apriori algorithm**

Hash-based technique

Transaction reduction

Partitioning

Sampling

Dynamic item set counting

**24.Define FP growth.(with out
candidate generation)**

An
interesting method in this attempt is called frequent-pattern growth, or simply
FP-growth, which adopts a *divide-and-conquer*
strategy as follows. First, it compresses the database representing frequent
items into a frequent-pattern tree, or FP-tree, which retains the item set
association information. It then divides the compressed database into a set of *conditional* *databases*

**25. What are Bayesian
classifiers?**

Bayesian
classifiers are statistical classifiers they can predict the class membership
probabilities that give tuples belongs to particular class

**26 What is rule based classifier**

It uses
set of IF-THEN rules for classification rules can be extract from a decision
tree rule may be generated from training data using sequential covering
algorithm and associative classification algorithm

**27. What is rule?**

Rules are
a good way of representing information or bits of knowledge. A rule-based
classifier uses a set of IF-THEN rules for classification. An IF-THEN rule is
an expression of the form

IF *condition* THEN *conclusion*. An example is rule *R*1,

R1: IF *age* = *youth* AND *student* = *yes* THEN *buys computer* = *yes*.

**28.What is Backpropagation?**

It is a
neural network algorithm for classification that employs a method of gradient
descent. it searches for set of weight that can model the data so as to
minimize the mean squared distance between the network class prediction.

**29. Define support vector
machine.**

a
promising new method for the classification of both linear and nonlinear data..
It uses a nonlinear mapping to transform the original training data into a
higher dimension. it searches for the linear optimal separating hyperplane for
separation of the data using essential training tuples called support vectors

**30. What is associative
classification?**

It uses a
association mining technique that search for frequently occurring patterns in
large database. the pattern may generate rules, which can be analyzed for
classification.

**31. Define Prediction with
classification.**

Prediction is similar to classification

First, construct a model^{}

^{ }

Second, use model to predict unknown value^{}

^{ }

ii).
Major method for prediction is regression

Linear and multiple regression^{}

^{ }

Non-linear regression^{}

^{ }

Prediction is different from classification

Classification refers to predict categorical class
label^{}

^{ }

Prediction models continuous-valued functions^{}

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Data Warehousing and Data Mining : Association Rule Mining and Classification : Important Short Questions and Answers : Association Rule Mining and Classification |

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