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Data mining is the process of extracting out valid and unknown information from large databases and use it to make difficult decisions in business (Gregory, 2000).Data mining or data analysis with complex and large datasets brings the wealth of research and knowledge in machine learning and statistics for the task of discovering new sets of knowledge in large databases. Over the past three decades, large amounts of difficult data's of business are stored electronically and this volume will continue to increase in future. In order to manage huge volumes of data, the techniques of data mining are also becoming sophisticated and advanced, day by day.
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Furkan, Salman Page 66
An Introduction to Data Mining Technique
Mohd. Furkan
1
, Agha Salman Haider
2
¹Sr Lecturer, Jazan University, Saudi Arabia
²
Sr Lecturer, Jazan University, Saudi Arabia
Abstract
Data mining is the process of extracting out valid and unknown information from large
databases and use it to make difficult decisions in business (Gregory, 2000).Data mining or
data analysis with complex and large datasets brings the wealth of research and knowledge in
machine learning and statistics for the task of discovering new sets of knowledge in large
databases. Over the past three decades, large amounts of difficult data’s of business are stored
electronically and this volume will continue to increase in future. In order to manage huge
volumes of data, the techniques of data mining are also becoming sophisticated and
advanced, day by day
.
Keywords—Data Mining ,DDM, Data Miners,
I
NTRODUCTION
Smyth, Mannila and Hand (2001) have defined that “progress in digital acquisition storage
technology has resulted in the development of vast database. This has happened in all areas of
human attempt from the mundane to the exotic. Little wonder then that attention has
development in the possibility of tapping these data, of demanding from them information
that might be of value to the proprietor of the database. The regulation concerned with this
assignment has become recognized as data mining. Defining a scientific discipline is always a
contentious task; researchers often disagree about the exact range and limits of their field of
study. Bearing this in and tolerant that others might oppose about the details, they shall
accept as their functioning definition of data mining”.
Data mining is the analysis of observational data sets to get unsuspected relationships and to
sum up the data in novel ways that are both understandable and useful to the data proprietor.
Furkan, Salman Page 67
The relationships and summaries derived throughout a data mining exercise are habitually
referred to as patterns or models. Illustrations include linear equations, graphs, tree structures,
clusters, rules and recurrent patterns in time series. The description over refers to
observational data as opposed to investigational data. Data mining classically deals with data
that have already been composed for various reasons other than the data mining analysis.
This means that the objectives of the data mining implement play on role in the data
collection plan. This is single way in which data mining is at variance from much of statistics
in which data are frequently composed by using well-organized strategies to reply particular
questions (Tan, Kumar and Steinbach, 2006).
Data mining techniques:
1. Web data mining:
The last decade has witnessed the web revolution which has ushered a new information
retrieval age. The revolution has had a profound impact on the way they search and find
information at home and at work. Searching the web has become an everyday experience for
millions of people from all over the world. From its beginning in the early 1990s the web had
grow to more than four billion pages in 2004 and perhaps would grow to more than eight
billion pages by the end of 2006.
Figure 2.9: Web data mining
Source: articlesweb.org
Furkan, Salman Page 68
2. Multi-Relational Data mining:
Chong, Feng and Cao (2010) have described that “Multi-relational classification is an
important data mining task, since much real world data is organized in multiple relations. The
major challenges come from firstly, the large high dimensional search spaces due to many
attributes in multiple relations and secondly, the high computational cost in feature selection
and classifier construction due to high complexity in the structure of multiple relations.
Mining multi-relational data repositories is an essential task in many applications such as
business intelligence. Multi-relational classification is arguably one of the fundamental
problems in multi-relational data mining. Multi-relational classification is challenging. First,
there may be a large number of attributes in a multi-relational database where classification is
conducted. Since, relations are often connected in one way or another; virtually multi-
relational classification has to deal with a very high dimensional search space.
3. Distributed data mining:
Pralhad, Ramachandrarao and Adhikari (2010) have explained that “Distributed Data Mining
(DDM) algorithms deals with mining multiple databases distributed over different
geographical regions. In the last few years, researchers have started addressing problems
where databases stored at different places cannot be moved to a central storage area for
variety of reasons. In multi-database mining there are no such restrictions. Thus distributed
data mining could be considered as a special type of multiple database mining. Distributed
data mining environment often comes with different distributed sources of computation.
4. Graph mining:
Kantardzic (2011) has described that “Graph mining applications are far more challenging to
implement because of the additional constraints that arise from the structural nature of the
underlying graph. The problem of frequent pattern mining has been widely studied in the
context of mining transactional data. Recently, the techniques for frequent pattern mining
have also been extended to the case of graph data”.
Furkan, Salman Page 69
Figure 19: Graph mining
Source: cdacmumbai.in
5. Visual data mining:
Kimani, Dix and Catarci (2010) have explained that” visual data mining is the use of
visualization technique to allow data miners and analysts to evaluate, monitor and guide the
inputs, products and process of data mining”. The field of visual data mining primarily
around the exploitation of the human visual system in mining knowledge. In essence, this can
be realized by placing the user at a strategic place in the system framework while the same
time exploitation effective visual strategies. Visual data mining may therefore, be fined as the
exploitation of appropriate visual strategies in order to allow enable or empower the data
mining user to process data and also to drive guide or direct entire process of data mining.
Figure 4.10: visual data mining
Furkan, Salman Page 70
C
ONCLUSION
This study explores the evolution of data mining, we have discussed different data mining
techniques used for data mining, we will discussed the best data mining technique in our
next paper.
R
EFERENCES
Smyth, Mannila and Hand (2001), Principles of data mining, MIT Press, p 1.
Tan (2007), Introduction to Data Mining, Pearson Education India, p 1.
Hand (1998). “Data Mining Statistics and More?”, The American Statistician, USA, p
112–118
Cerrito (2006), Introduction to data mining using SAS Enterprise Miner, SAS
Publishing, p 1.
Larose (2005), Discovering knowledge in data: an introduction to data mining, John
Wiley and Sons, p 2.
Sullivan (2011), Introduction to Data Mining for the Life Sciences, Springer, p 2.
Pei, Kamber and Han (2011), Data Mining: Concepts and Techniques, Elsevier, p 1.
Sivanandam and Sumathi (2006), Introduction to data mining and its applications,
Springer, p 2.
... ere are various data mining algorithms and techniques available to transform raw data into useful information such as association rules, SVM, neural networks, K-nearest neighbor, and decision trees. We will use SVM and decision tree in this study for the identification and classification of information [16]. ...
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to Data Mining Principles.- Data Warehousing, Data Mining, and OLAP.- Data Marts and Data Warehouse.- Evolution and Scaling of Data Mining Algorithms.- Emerging Trends and Applications of Data Mining.- Data Mining Trends and Knowledge Discovery.- Data Mining Tasks, Techniques, and Applications.- Data Mining: an Introduction - Case Study.- Data Mining & KDD.- Statistical Themes and Lessons for Data Mining.- Theoretical Frameworks for Data Mining.- Major and Privacy Issues in Data Mining and Knowledge Discovery.- Active Data Mining.- Decomposition in Data Mining - A Case Study.- Data Mining System Products and Research Prototypes.- Data Mining in Customer Value and Customer Relationship Management.- Data Mining in Business.- Data Mining in Sales Marketing and Finance.- Banking and Commercial Applications.- Data Mining for Insurance.- Data Mining in Biomedicine and Science.- Text and Web Mining.- Data Mining in Information Analysis and Delivery.- Data Mining in Telecommunications and Control.- Data Mining in Security.