Download What is data mining - 2010-CS-A

Department of Computer Science
Sir Syed University of Engineering &
Technology, Karachi-Pakistan.
Presentation Title:
DATA MINING
Submitted By
What is data mining ?
 Data mining consists of five major elements
 Why Mine Data?
 Commercial Viewpoint
 Scientific Viewpoint
 Some of the techniques used for data mining

Data Mining, also known as KnowledgeDiscovery in Databases (KDD), is the process
of automatically searching large volumes of
data for patterns.
 It is the process of extraction of knowledge
from large datasets.
 Extremely large datasets.
 Useful knowledge that can improve
processes.
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Extract, transform, and load transaction data
onto the data warehouse system.
Store and manage the data in a
multidimensional database system.
Provide data access to business analysts and
information technology professionals.
Analyze the data by application software.
Present the data in a useful format, such as a
graph or table.

Lots of data is being collected
and warehoused
 Web data, e-commerce
 purchases at department/
grocery stores
 Bank/Credit Card
transactions
 Computers have become cheaper
and more powerful

Competitive Pressure is Strong
 Provide better, customized services
for an edge (e.g. in Customer
Relationship Management)

Data collected and stored at
enormous speeds (GB/hour).
 remote sensors on a satellite
 telescopes scanning the skies
 microarrays generating gene
expression data
 scientific simulations
generating terabytes of data
Traditional techniques
infeasible for raw data.
 Data mining may help
scientists .

 in classifying and segmenting
data

Artificial neural networks - Neural networks
are useful for pattern recognition or data
classification, through a learning process.
Non-linear predictive models that learn
through training and resemble biological
neural networks in structure.

Neural Networks map a
set of input-nodes to a
set of output-nodes

Number of
inputs/outputs is variable

The Network itself is
composed of an arbitrary
number of nodes with an
arbitrary topology
Input 0
Input 1
...
Input n
Neural Network
Output 0
Output 1
...
Output m
 Tree-shaped structures that
represent sets of decisions. These
decisions generate rules for the
classification of a dataset.
height
short
tall
tall
short
tall
tall
tall
short
hair
blond
blond
red
dark
dark
blond
dark
blond
eyes
blue
brown
blue
blue
blue
blue
brown
brown
class
A
B
A
B
B
A
B
B
hair
dark
blond
red
B
A
eyes
blue
A
brown
B
A classification technique that
classifies each record based on the
records most similar to it in an
historical database.
•
Clustering can be considered the most
important unsupervised learning technique;
so, as every other problem of this kind, it
deals with finding a structure in a collection
of unlabeled data.
•
Clustering is “the process of organizing
objects into groups whose members are
similar in some way”.
•
A cluster is therefore a collection of objects
which are “similar” between them and are
“dissimilar” to the objects belonging to other
clusters.
The greater the similarity (or homogeneity)
within a group, and the greater the difference
between groups, the “better” or more distinct
the clustering.
A few good reasons ...
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Simplifications
Pattern detection
Basic K-means Algorithm for finding K clusters:
1. Select K points as the initial centroids.
2. Assign all points to the closest centroid.
3. Recompute the centroid of each cluster.
4. Repeat steps 2 and 3 until the centroids don’t
change.