TOPICS IN DATA SCIENCE
Submitted to :-AbdolrezaAbhari
Submitted by :- Gurpreet
information from the pool of data is termed as data mining. There is humungous
data available in the information industry that is useless unless converted
into beneficial information and analyzed to discover any fraudulence, buyer’s
choice, to control the manufacturing of products and understand the market
mining helps the entrepreneurs to know their customers better in a way of their
choices, the deals for their money, their income and criteria by which they
like to spend. It also gives an idea how often a customer likes to spend and
makes one capable to relate different people with similar choices.
from these it also assists in cooperate sector.
mining is categorized as “Descriptive and Classification and production” on the
basis of the type of the data.
It describes the basic feature of information in database such as:
-Mining of frequent patterns
-Mining of association
-Mining of correction
-Mining of clusters
Class- The products to be sold by the company, for example, clothes.
Concept- The money being spent by the customer, shoppers or the ones who buy in
They can be gathered in
– Data Characterization: Review the data of the class to be studied namely the ‘Target
– Data Discrimination: Comparison of the class with a designated class.
MINING OF FREQUENT PATTERNS
The products (patterns) that usually are seen in transactional data are
termed as frequent patterns.
– Frequent item set: The products that are enlisted with one another such as
top and bottom wear in clothing section.
– Frequent sub sequence: The products that are generally bought with the main
item such as buying pet food followed by pet treats.
-Frequent sub structure: Graphs, trees or various other structural forms that
are attached to sub sequences.
MINING OF ASSOCIATION
The item that are generally bought together are included in this category. With
the help of this a businessman discovers a percentage of association between
products bought together such as 60 percent of times a mobile phone is bought
with a mobile cover and 40 percent of times with screen guards.
MINING OF CORRELATION
It reveals the effect of purchase of one product over another whether it has a
negative, positive or no effect at all.
MINING OF CLUSTERS
It is grouping the like similar products from one another. Each cluster
varies from the other.
class label of some items may be unknown. Classification and prediction is one
such procedure that can be utilized to uncover the data class or concepts.
This procedure is presented as:
(a) Classification (If-Then) rules
(b) Decision trees
(c) Mathematical formulae
(d) Neural networks
model that differentiates the class or concept of the information. This model
is based on the object with a well known class label.
– Prediction: Regression analysis is brought to practice to predict the
numerical values that are unknown rather than the class label. Also it is used
to identify sale trends on the basis of data available.
-Outlier analysis: The data that does not abide by the model of data available
is an outlier data.
-Evolution analysis: It
refers to those subjects which are transitional in nature.
HOW DOES THE CLASSIFICATION WORK?
incorporates two stages:
-Building the classifier or model
– Using classifier for classification
BUILDING THE CLASSIFIER
-It is a
calculations assemble the classifier
-set made from
database tuples and related class labels
-each type is
called as classification or class are known as test/question or information
USING THE CLASSIFIER
Classifier is utilized for arrangements that include
analyzing the relevance and exactness of characterization rules and thus
linking the older and new information tuples if considered adequate.
DATA MINING TASK
A data mining exercise can be specified as a query.
-Transfer the query to the computer.
-This query is hence derived as data mining task primitive.
-Therefore, the primitive develop an interactive
communication with data mining system.
This process is undertaken with following requirements:
#Mine the appropriate data:
Part of database that is of user’s interest.
It is composed of:-
database attributes and data warehouse dimensions of
#Nature of information for mining process
It advices the functions to be undertaken which are:
and correlation analysis
It permits the mining of information at multifarious levels
E.g. the concept of hierarchies.
#Effectiveness measures and outset for evaluation for the
The patterns discovered through stored knowledge are
anticipate the uncovered patterns
It alludes to the visualization of discovered patterns by the
means of rules, tables, charts, decision trees, graphs etc.
ISSUES IN DATA MINING
Data aggregation can be complicated due unavailability of
information all at a single place. It creates a need to be collected from
The major points of concern are:
(i)Mining methodology and user interest
(iii)Diverse data type issues
The following diagram shows issues in data mining:
In order to back the discussion of management following
features are exhibited:
Since the information related to subject that could be sales,
customer, product etc, so data warehouse is considered as subject oriented. In addition,
it does not consider the prevalent operation but the analysis of data for
Since the data is
collected from variable sources, it makes it reliable for studying the data.
The data is recognized in relation to the past view points.
Data warehouse is kept aloof the operational database. So any
new information does not delete or replace the previously stored information.
Data warehousing is composed of data cleaning, integration
and consolidation and is followed through two approaches i.e. query driven and
update driven viz a viz the former builds the wrappers and integrations also
called mediators and the latter makes the data available for direct query. Update
driven approach is today’s approach.
Data mining is used in:
Financial data analysis
Biological data analysis
Other scientific applications
Data mining in banking/finance
financial arena data mining is reliable to predict payment of the loans and
analysis of the credit policy and detect any fraudulence.
Data mining in marketing
in retail industry it helps in better understanding of customers, products,
Data mining in healthcare
helps preserve a large data as in bioinformatics that enables study in various
biological aspects such as genomics, proteomics and biomedical research.
TRENDS IN DATA MINING
is a constant evolution of concept in data mining such as follows:
Exploring the application
Distributed data mining
Data Mining: Practical Machine Learning Tools and
Techniques, Elsevier Science, 2011