Data mining is
described as the extraction of hidden helpful information from a collection of
huge databases, data mining is also a technique that encompasses an enormous
form of applied mathematics and compultational techniques like link
analysis,clustering, classification, summarizing knowledge , regression
analysis and so on. data mining tools predict future trends and behaviors,
permitting businesses to create knowledge-driven selections. The
machine-driven, prospective analyses offered by data mining move on the far
side the analyses of past events. data mining tools provides answer to business questions that were time
consuming. They search databases for hidden patterns, finding useful
information that is beyond the reach of specialists.
Data mining techniques
is enforced speedily on existing package and hardware platforms to reinforce
the worth of existing information resources, and might be integrated with new
product and systems as they're brought. once enforced on high performance
client/server or multiprocessing computers, data mining tools will analyze huge
databases to provide answers to questions such as, ”What goods consumers tend
to buy the most and goods that go along
side with it”.
his publication” Data Mining: Past, Present and Future” discussed the history
of data mining can be dated as far back as late 80s when the term began to be used, at
least within the
research community and diffrentiated it from sql.
mining can be defined as as set of mechanisms and techniques, realised in
hidden information from data. However,the word hidden in this definition is
By the early
1990s data mining was commonly recognised as a sub process within a larger
process called Knowledge Discovery in Databases or KDD ,the most commonly used
definition of KDD is that of Fayyad et al
as “the nontrivial process of identifying valid, novel, potentially
useful and ultimately understandable patterns in data.’’ (Fayyad et al. 1996).
As such data mining
should be viewed as the sub-process, within the overall KDD process, concerned
with the discovery of hidden information". Other sub-processes that form
part of the KDD process are data preparation (warehousing, data cleaning,
pre-processing,and so on) and the analysis/visualisation of results. For may
practical purposes KDD and data mining are seen as synonymous, but technically
one is a sub-process of the other. The data that data mining techniques were
originally directed at was tabular data and, giventhe processing power available
at the time, computational eficiencywas of significant concern. As the amount
of processing power generally available increased, processing became less of a
concern and was replaced with a desire for accuracy and a desire to mine ever
larger data collections. Today, in the context of tabular data, we have a well
established range of data mining techniques available.
It is well
within the capabilities of many commercial enterprises and researchers to mine
data, using software
such as Weka, on standard desktop
machines. However,the amount of electronic data collected by all kinds of
institutions and commercial enterprises,year on year, continues to grow and
thus there is still a need for efective mechanisms to mineever larger data
sets. The popularity of data mining increased significantly in the 1990s,
notably with the establishment of a number of dedicated conferences; the ACM SIGKDD(special
intrest group on knowledge discovery in data) annual conference in 1995, and
the European PKDD(practice of knowledge discovery in databases) and the Pacific/Asia
PAKDD(pacific asiaconference on knowledge discovery and data mining) conferences
This increase in popularity can be attributed to advances in technology; the
computer processing power and data storage capabilities available meant that
the processing of large volumes of data using desktop machines was a realistic
possibility. It became common place for commercial enterprises to maintain data
in computer readable form, in most cases this was primarily tosupport
commercial activities, the idea that this data could be mined often came
second. The 1990s also saw the introduction of customer loyalty cards that allowed enterprises to record customer
purchases, the resulting datacould then be mined to identify customer
purchasing patterns. Data mining , is the method of looking into giant volumes
of data for patterns using methods like classification, association rule
mining, clustering, etc.. data mining is a topic that is related to topics like machine learning and pattern
recognition. data mining techniques area unit the results of an extended
process of analysis and products development.
I am in my final year.
I was bright and brilliant, my family was optimistic in me; they thought so much of
me, but I had a fault. What was my fault? I hated compiler construction. I struggled with calculations all my life. Though
i have been lucky; I did well all the
same. However, I had to write my final exam. I searched for all Compiler construction
past question for each year, compared, and sorted them. Guess what I
discovered! Over 35% of the questions were repetitions. I had hit the jackpot.
I carefully and thoroughly checked through the answer page. Therefore, I kept
on revising only the repeated questions. Well, I have a good grade to show for
the Data Mining I performed.
There is huge amount of
data available in Information Industry. This data is of no use until converted
into useful information. Analyzing this huge amount of data and extracting
useful information from it is necessary. The extraction of information is not
the only process we need to perform; it also involves other processes such as
Data pre-processing( Data Cleaning, Data Integration, Data Transformation) Data
Mining, Pattern Evaluation and Data Presentation. Once all these processes are
over, we are now position to use this information in many applications such as
Fraud Detection, Market Analysis, Production Control, Science Exploration etc.
Through in depth
research and observations carried on supermarket we have discorvered that
retailers are willing to know what product is purchased with the other or if a
particular products are purchased together as a group of items. Which can help
in their decision making with respect to
placement of product , determining the
timing and extent of promotions on product
and also have a better understanding of customer purchasing habits by
grouping customers with their
This project is aimed
at designing and implementing a well-structured market basket analysis software
tool to solve the problem stated above and compare the result to that of an
existing software called WEKA.
OF THE STUDY
AND OBJECTIVE OF THE STUDY
aim of the study is to maximize profit
for the retailers by providing better
services to the consumers
objective of this study are:
Cross-Market Analysis - Data Mining
performs Association/correlations between product sales.
Identifying Customer Requirements -
helps in identifying the best products for differentcustomers. It uses
prediction to find the factors that may attract new customers.
Customer Profiling - helps to determine
what kind of people buy what kind of products.
to the fact that the data we are getting is a raw data,raw data in the real
world may be incomplete it has to be
pre-processed the raw data has to go through data cleaning,data integration,data
normarlization,data reduction because without a quality data there will be
no quality mining results.
cleaning:This has to do withfilling of missing values, resolving of inconsistencies in the raw data.
from multiple sources and generating the user with unified view of the data
normalization is used to minimize or to reduce
reduction:reduction of the data set
that is much smaller in volume but yet yields the same analytical results
OF THE STUDY
This scope of the
study focuses on Babcock Ventures supermarket and the scope of this project
aim to develop our very own market basket analysis software, which will be used
in babcock university
software will exhibit a colorful GUI(graphical user interface).
software will be based on Apriori .
intend to conduct a research into the various branches of science that this
software will be based on, such as artificial intelligence.
will develop a software that will eventually stand out among other data mining
OF THE STUDY
The limitations of this
software will include:
restrictions:this is a major factor that stands in
the way of the execution of this project.Since there is no data on households
and individual consumers ,we neglect such purchases.
2. Time constraints: this
is also a major factor due to the fact that it can’t work on a small amount of
raw data because it tends to mislead the retailer in a nut shell this software
will work on large volumes of data.