rnn appraoch for machine learning with datamining.pptx

A Recurrent Neural Network
Approach for Feature Selection using
Cloud Database

Introduction
Combination of Cloud Computing, Neural
Network and Feature Selection.
Datasets stored in the cloud database are
retrieved from the cloud environment
 train the data sets using feed-back neural
network by applying proposed feature selection
method to achieve the target concepts.
better than the Conventional models
handles noisy data and removes redundant,
irrelevant features while considering feature
interaction.

Data Mining
Nontrivial extraction of implicit, previously
unknown and potentially useful information
from data in databases.
Also known as KDD (Knowledge Discovery in
Databases )

Steps in KDD
Data cleaning
Data integration
Data selection
Data transformation
Data mining
Pattern evaluation
Knowledge representation

Architecture of a typical Data mining
System

Data Mining Techniques
 Association
Classification
Clustering
Prediction
Decision Trees
Neural Networks

Data Preprocessing
Incomplete, noisy, and inconsistent data are
commonplace properties of large real world
databases and data warehouses.
Duplicate tuples also require data cleaning
Need for data reduction
 A database/data warehouse may store terabytes of data
 Complex data analysis/mining may take a very long
time to run on the complete data set

Data reduction strategies
• Data cube aggregation
• Dimensionality reduction — e.g., remove
unimportant attributes
• Data Compression
• Numerosity reduction — e.g., fit data into
models
• Discretization and concept hierarchy generation

Dimensionality Reduction Technique
Principal component analysis
Singular value decomposition
Supervised and nonlinear techniques (e.g.,
Feature Selection)

Feature Selection
pre-processing step in data mining process
which selects the most “relevant” subset of
attributes according to some selection criteria.
Two Commonly used approaches followed in
feature selection are
Wrapper approach
Filter approach
Wrapper approach is generally more accurate
but also more computationally expensive.

Feature Selection Algorithm
General Algorithm for Feature Selection
Input:
S- data sample with features X, |X| = n
J – Evaluation measure to be Maximized
GS – Successor generation Operator
Output:
Solution – (Weighted) feature subset
L := Start_Point(X);
Solution = { best of L according to J};
repeat
L:= Search_Strategy( L, GS(J),X);
X’
:= { best of L according to J};
if J(X’)>=J(Solution) or (J(X’) = J(Solution) and |X’|< |Solution|)
then Solution := X’;
until Stop(J,L)

rnn appraoch for machine learning with datamining.pptx

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