Data Mining Examples and Techniques

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Data Mining Examples and Techniques
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Data Mining
Data mining is an extraction of
interesting (potentially useful) or knowledge
from the massive amount of data. The wide
availability of vast amounts of data and the
imminent need for turning such data into useful
information and knowledge. Data mining is defined
as a process used to extract usable data from a
larger set of any raw data which implies
analysing data patterns in large batches of data
using one or more software.
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Real life Examples in Data Mining
Following are
the various real-life examples of data mining, 1.
Shopping Market Analysis  There is a huge amount
of data in the shopping market, and the user
needs to manage large data using different
patterns. Market basket analysis is a modelling
technique is used to do the analysis. Market
basket analysis is a modelling technique based on
a theory that if you buy a group of items, you
are more likely to buy another group of things.
This technique may allow the retailer to
understand the purchase behaviour of a buyer.
Using differential analysis comparison of results
between different stores, between customers in
different demographic groups can be done.
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2. Stock Market Analysis There is a vast
amount of data to be analysed in the stock
market. So, data mining technique is used to
model those data to do the analysis. 3. Weather
forecasting analysis Weather forecasting system
uses an enormous amount of historical data for
prediction. As there is a processing of enormous
amount data, one must have to use the suitable
data mining technique. 4. Fraud Detection Due to
the size of the data, traditional methods of
fraud detection are time-consuming and
complicated. Data mining helps in providing
meaningful patterns and turning data into
information.
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5. Intrusion Detection  Data mining can help
to improve intrusion detection by adding a level
of focus to anomaly detection. It supports an
analyst to distinguish activity from common
everyday network activity. 6. Financial Banking A
tremendous amount of data is supposed to be
generated with new transactions in computerised
banking. Data mining can donate to solving
business problems in banking and finance by
finding patterns, causalities, and correlations
in business information
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7. Surveillance Video surveillance is used in
a day to day life almost at every place for
security perception. Data mining is used in video
surveillance as we need to deal with a large
amount of collected data. 8. Online Shopping In
online shopping, E-commerce companies like
Amazon, Flipkart, Snapdeal, Myntra, and many more
uses Data Mining and Business Intelligence to
offer cross-sells and up-sells through their
websites, who use sophisticated mining techniques
to drive there, People who viewed that product,
also liked this functionality. Data mining is
used to identify customers loyalty by analysing
the data of customers purchasing activities such
as the data of frequency of purchase in a period,
a total monetary value of all investments and
when was the last purchase.
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9. Criminal Investigation Criminal
Investigation is a process that intentions to
identify crime characteristics. Crime analysis
includes discovering and detecting crimes and
their relationships with criminals. The large
volume of crime datasets and the complexity of
relationships between them have made criminology
a suitable field for applying data mining
techniques. 10. Bioinformatics  Data Mining
approaches are well suited for Bioinformatics, as
it contains a massive amount of data. The mining
of biological data aids to extract useful
knowledge from massive datasets gathered in
biology, and other related life sciences areas
such as medicine and neuroscience.
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11. Health Care and Insurance The growth of
the insurance industry entirely depends on the
ability to convert data into the knowledge,
information or intelligence about customers,
competitors, and its markets. Data mining is
applied in insurance industry lately but brought
tremendous competitive advantages to the
companies who have implemented it successfully.
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Data Mining Techniques
Data Mining techniques
are as follows, 1. Classification Analysis
Technique  Classification technique is used for
assigning the items into target categories or
classes which is used to predict what will occur
within the class accurately. It classifies each
item in a set of data into one of a predefined
set of classes or groups. We use it to classify
different data in different classes. As this
process is like clustering. It relates a way that
segments data records into different segments
called classes. An example is an Outlook email.
They use specific algorithms to characterise an
email as authenticating or spam.
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Figure A classification model can be
represented in various forms, such as (a) IF-THEN
rules, (b) a decision tree, or a (c) neural
network 
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Association Rule Learning Technique
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• 
  
• It is also known as relation technique.
• A pattern is recognised based upon the
  relationship of items in a single transaction.
• The association technique is used in market
  basket analysis to identify a set of products
  that customers frequently purchase together.
• Retailers used the association technique to
  research customers buying habits. Based on
  historical sale data, retailers might find out
  that customers always buy crisps when they buy
  beers, and, therefore, they can put beers and
  crisps next to each other to save time for the
  customer and increase sales.

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• 3. Anomaly or Outlier Detection Technique
• Outliers is defined as the data objects that do
  not comply with the general behaviour or model of
  the data available.
• It refers mainly to an observation of data items
  in a dataset for the data sets that do not match
  an expected pattern.
• Anomalies are also known as outliers, novelties,
  noise, deviations, and exceptions as this anomaly
  provide critical and actionable information
• 4. Clustering Analysis Technique
• Cluster analysis is one of the techniques of data
  mining by which related records are grouped. As a
  result, objects are like one another within the
  same group. Although, they are different in same
  or other clusters.
• The objects are clustered based on the principle
  of maximising the intraclass similarity and
  minimising the interclass similarity.
• In clustering, the class labels are not present
  in the training because they are not known to
  begin with which is called unsupervised learning.

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5. Regression Analysis Technique 
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• This technique is used for establishing the
  dependency between the two variables so that
  causal relationship can be used to predict the
  outcome.
• In statistical ways, we use to identify and
  analyse the relationship between variables.
• It helps you to know the characteristic value of
  the dependent variable.
• Generally, used for prediction and forecasting. 
• 6. Prediction Technique
• Prediction is made by finding the relationship
  between independent and dependent variables.
• Suppose the deal is an independent variable and
  profit could be a dependent variable. Then we can
  draw a fitted regression curve that is used for
  profit prediction. 

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• 7. Sequential Patterns Technique
• This is an important part of data mining
  techniques.
• This technique will identify regular occurrences
  of similar events.
• This technique is used to understand user buying
  behaviours. With the help of historical data.
• This technique is used in shopping basket
  application.
• In online shopping sales, with the use of
  historical transaction data, businesses can
  identify a set of items that customers buy
  together different times in a year. Then
  companies can use this information to recommend
  customers buy it with better deals based on their
  purchasing frequency in the past. 

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8. Decision Trees Technique
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• decision tree is one of the analytical technique
  of Data Mining.
• This technique is effortless to understand the
  users.
• This technique is used for categorising or
  predict data.
• In this technique, the root of a decision tree is
  a simple question. As they have multiple answers.
• Above figure shows an example where you can
  classify an incoming error condition.

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