Harmonized scheme for data mining technique to progress decision support system in an uncertain situation

IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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Volume: 01 Issue: 03 | Nov-2012, Available @ http://www.ijret.org 335
HARMONIZED SCHEME FOR DATA MINING TECHNIQUE TO
PROGRESS DECISION SUPPORT SYSTEM IN AN UNCERTAIN
SITUATION
B.Swathi1
, M.Praveena2
, L.Kavitha3
1
Nri Institute of technology, CSE Dept, 2
Nri Institute of technology, IT department, 3
Nri institute of technology, MCA
Department
Abstract
Decision Support System (DSS) is equivalent synonym as management information systems (MIS). Decision supporting systems
include also decisions made upon individual data from external sources, management feeling, and various other data sources not
included in business intelligence. They serve as an integrated repository for internal and external data-intelligence critical to
understanding and evaluating the business within its environmental context. Data mining have emerged to meet this need. With the
addition of models, analytic tools, and user interfaces, they have the potential to provide actionable information that supports
effective problem and opportunity identification, critical decision-making, and strategy formulation, implementation, and
evaluation. The proposed system will support top level management to make a good decision in any time under any uncertain
environment.
Keywords: Dss, Dm, Mis, Clustering, Classification, Association Rule, K-Mean, Olap, Matlab
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1. INTRODUCTION
Decision Support System (DSS) is equivalent synonym as
management information systems (MIS). Decision supporting
systems include also decisions made upon individual data
from external sources, management feeling, and various other
data sources not included in business intelligence. With the
addition of models, analytic tools, and user interfaces, they
have the potential to provide actionable information that
supports effective problem and opportunity identification,
critical decision-making, and strategy formulation,
implementation, and evaluation. The proposed system will
support top level management to make a good decision in any
time under any uncertain environment [4]. This study aim to
investigate the adoption process of decision making under
uncertain situations or highly risk environments effecting in
decision of investing stoke cash of bank. This applied for two
types of usage investment - direct or indirect - or credit and
any sector of investment will be highly or moderate or low
risk. And select which one of this sectors risk „rejected‟ or un-
risk „accepted‟ all that under uncertain environments such as;
political, economical, marketing, operational, internal policies
and natural crises, all that using the contribution of this study
enhancing k-mean algorithm to improve the results and
comparing results between original algorithm and enhanced
algorithm. The paper is divided into four sections; section two
is a background and related work it is divided into two parts,
part one is about DSS, part two is about DM. Section three
presents the proposed Investing Data Mining System „IDMS.
Section four presents conclusion and finally section five
present future works2. Tables, Figures and Equations.
2. BACKGROUND AND RELATED WORK
2.1 Decision Support System (DSS)
DSS includes a body of knowledge that describes some
aspects of the decision maker's world that specifies how to
accomplish various tasks, that indicates what conclusions are
valid in different circumstances [4].The expected benefits of
DSS that discovered are higher decision quality, improved
communication, cost reduction, increased productivity, time
savings, improved customer satisfaction and improved
employee satisfaction. DSS is a computer-based system
consisting of three main interacting components:
• A language system: a mechanism to provide
communication between the user and other
components of the DSS.
• A knowledge system: A repository of problem
domain knowledge embodied in DSS as either data or
procedures.
• A problem processing system: a link between the
other two components, containing one or more of the
general problem manipulation capabilities required
for decision-making.

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Fig 1: DSS Main Components
After surveying multiple decision support systems, it is
concluded that decision support systems are categorized into
the following [5]:
• File drawer systems: This category of DSS provides
access to data items.
• Data analysis systems: Those support the
manipulation of data by computerized tools tailored
to a specific task or by more general tools and
operators.
• Analytical information systems: Those provide
access to a series of decision-oriented databases.
• Accounting and financial models: those calculate
the consequences of possible actions.
• Representational models: those estimate the
consequences of actions based on simulation models
that include relationships that are causal as well as
accounting definitions.
• Optimization models: those provide guidelines for
actions by generating an optimal solution consistent
with a series of constraints.
• Suggestion models: those perform the logical
processing leading to a specific suggested decision or
a fairly structured or well understood task.
This section describes the approaches and techniques mostly
used when developing data warehousing systems that data
warehousing approaches such as; Online Analytical
Processing „OLAP‟, Data Mining „DM‟ and Artificial
Intelligence „AI‟. Data Warehousing Techniques such as; Star
Schema, Snowflake Schema and A Star Schema or a
Snowflake Schema
2.2 Data Mining Techniques (DM)
Data mining is the process of analyzing data from different
perspectives and summarizing it into useful information [10].
DM techniques are the result of a long process of research and
product development [10]. The evolution of DM [6] is shown
in table 1.
Table 1: The evolution by DM [6]
Steps for applying DM:
1. Definition of the business objective and expected
operational environment.
2. Data selection is required to identify meaningful sample of
data.
3.Data transformation that involves data representation in an
appropriate format for mining algorithm.
4. Selection and implementation of data mining algorithm
depends on the mining objective.
5.Analysis of the discovered outcomes is needed to formulate
business outcomes.
6. Representing valuable business outcomes.

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Data mining consists of five major elements; to extract, to
transform, and to load transaction data onto the data
warehouse system ,to store and manage the data in a
multidimensional database system, to provide data access to
business analysts and information technology professionals,
Analyze the data by application software, and finally to
present the data in a useful format, such as a graph or table.
DM techniques usually fall into two categories, predictive or
descriptive. Predictive DM uses historical data to infer
something about future events. Predictive mining tasks use
data to build a model to make predictions on unseen future
events. Descriptive DM aims to find patterns in the data that
provide some information about internal hidden relationships.
Descriptive mining tasks characterize the general properties of
the data and represent it in a meaningful way. Figure2 shows
the classification of DM techniques.
Association Rule is used to disc over relationships between
attribute sets for a given input pattern. [6] Define sequence
discovery as "a sequential technique is a given set of
sequences find the complete set of frequent subsequences”.
Clustering is “the process of organizing objects into groups
whose members are alike in some way” [7]. So, it deals with
finding the internal structure in a collection of data, as shown
in figure 3.
Fig 3: Simple graphical for clustering data [7]
[8] Define that “Clustering involves identifying a finite set of
categories or segments „clusters‟ to describe the data
according to a certain metric". [9] Define that “Clustering
enables to find specific discriminative factors or attributes for
the studied data. Each member of a cluster should be very
similar to other member s in its cluster and very dissimilar to
other clusters. When a new data is introduced, it is classified
into the most similar cluster". Several researchers classified
clustering algorithms differently.
Some classifies clusters as mutually exclusive, hierarchical or
overlapping. Others classifies cluster into hierarchal and
partitional. The most common classification is shown in figure
3. Techniques for creating clusters include partitioning
methods as in k-means algorithm, and hierarchical methods as
in decision trees, and density-based methods.
Fig 4: Clustering methods classifications for Moses Charikar
[10]
3. THE PROPOSED 'IDM S'
Investment Data Mining System „IDMS‟ aims to build a data
mining system for investment in the banking sector. IDMS
consists of several components; data gathering, preparing data
to discover knowledge, data preprocessing, using data mining
techniques in sequences steps start with classification data,
clustering data especially using K-mean algorithm and
enhanced K-mean algorithm to set which best result and then
set and run association rules to solve problem, post processing
and finally get result and visualize result to create best
decision to take a good decision for investment under
uncertain situations. IDMS Shown in figure 5.
Hardware for applying the IDMC system is a personal
computer configurations with this Processor 3.2, Hard Disk
160 gaga, Ram 2 G and Monitor 17 Inch. Operating system is
windows XP services pack 3. Several software tools have been
used. The first is Microsoft Excel sheets 2007 and has been
used for analysis and filtering data. Mat-lab version 6.5 has
been used in data preprocessing and data classification. The

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last software is the WEKA which is a collection of java tools
for DM written by staff at the University Of Waikato, New
Zealand.
Fig 5: Proposed IDMS
CONCLUSIONS
This paper represents a contribution of applying DM and DSS
for banking sector especially in investment which has been
rarely addressed before. IDMS is a new proposed system
which is simple, straightforward with low computation needs.
The proposed preprocessing component is an aggregation of
several known steps. The banking is planning a set of actions
in accordance of IDMS outcomes for decision making in
investment sector. The investment department in the banking
is starting to analyze the approached investment sector, to
introduce a good decision under uncertain situation.
FUTURE WORK
In next step of this study implementing this proposed approach
and creating full steps of preprocessing using enhanced k-
mean algorithm by real data from bank as a case study, to give
us a best result and support high level of management with a
good decision. After implementing this case study and
proposed system will get the main factors which effect in
decision under uncertain
REFERENCES
[1] A. Hunter and S. Parsons, "A review of uncertainty
handling formalisms", Applications of Uncertainty
Formalisms, LNAI 1455, pp.8-37. Springer -Verlag, 1998.
[2] E. Hernandez and J. Recasens, "A general framework for
induction of decision trees under uncertainty", Modelling with
Words, LNAI 2873, pp.26–43, Springer-Verlag, 2003.
[3] M. S. Chen, J. Han, and P. S. Yu. IEEE Trans Knowledge
and Data Engineering Data mining. An overview from a
database perspective, 8:866-883, 1996.
[4]. U. Fayyad, G. Piatetsky-Shapiro and W. J. Frawley.
AAAI/MIT, Press definition of KDD at KDD96. Knowledge
Discovery in Databases, 1991.
[5]. Gartner. Evolution of data mining, Gartner Group
Advanced Technologies and Applications Research Note,
2/1/95.
[6]. International Conferences on Knowledge Discovery in
Databases and Data Mining (KDD‟95-98), 1995-1998.
[7]. R.J. Miller and Y. Yang. Association rules over interval
data. SIGMOD'97, 452-461, Tucson, Arizona, 1997.
[8]. Zaki, M.J., SPADE An Efficient Algorithm for Mining
Frequent Sequences Machine Learning, 42(1) 31-60, 2001.
[9]. Osmar R. Zaïane. “Principles of Knowledge Discovery in
Databases - Chapter 8 Data Clustering”. & Shantanu Godbole
data mining Data mining Workshop 9th November 2003.
[10]. T.Imielinski and H. Mannila. Communications of ACM.
A database perspective on knowledge discovery, 39:58-64,
1996.
[11]. BIRCH Zhang, T., Ramakrishnan, R., and Livny, M.
SIGMOD '96. BIRCH an efficient data clustering method for
very large databases. 1996.
[12] Pascal Poncelet, Florent Masseglia and Maguelonne
Teisseire (Editors). Information Science Reference. Data
Mining Patterns New Methods and Applications, ISBN 978
1599041629, October 2007.
[13]. Thearling K, Exchange Applications White Paper, Inc.
increasing customer value by integrating data mining and
campaign management software, 1998.
[14]. Noah Gans, Spring. Service Operations Management,
Vol. 5, No. 2, 2003.
[15]. Joun Mack. IEEE TRANSACTIONS ON PATTERN
ANALYSIS AND MACHINE INTELLIGENCE. An Efficient
k-Means Clustering Algorithm, Analysis and Implementation,
VOL. 24, NO. 7, JULY 2002.
[16]. Andrew Moore and Brian T. Luke. Tutorial Slides, K-
means and Hierarchical Clustering and K-Means Clustering,
Slide 15, 2003.
[17]. E. Turban, J. E. Aronson, T. Liang, and R. Sharda,
Decision Support and Business Intelligence Systems, eighth
edition. Prentice Hall, 2007.

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