The Development of Data Mining - International Journal of Business

The Development of Data Mining - International Journal of Business
International Journal of Business and Social Science
Vol. 4 No. 16; December 2013
The Development of Data Mining
Fang Weiping
Wang Yuming
Shanghai Engineering Technology University İnstitute of Management
Shanghai, 201620
Mining is the current hot spots, the most promising research areas has broad one, through data mining research
status, algorithms and applications of analysis to explore data mining problems and trends, which is the
development of data mining has certain reference value.
Key Words: Database; evolution; algorithm
1. Introduction
In our daily life, we often encounter such a situation: message tell you XXX store or e-commerce platform will
present new products or promotions recently, or emails notify you about mall anniversary celebration recently.
This is because retailers at the store checkout the customer data and collect their data, retailers can make use of
this information, combined with electronic commerce log , shopping record and so on, better understanding of the
needs of the consumers, to make a correlation analysis. In the movement of the era of big data, data analysis tools
in the information management system have not been extracted information from huge complex data. For
example, information systems can not analysis of these data which are collected and observe from satellite
surface, Marine atmospheric, because of the size and characteristics of time and space. Also, there are vast
amounts of genetic data, etc. In this way, we need to develop a new method to mine the data.
2. Concept of data mining
Data Mining is the advanced process which extracts the potential and effective and comprehensive mode from the
vast amounts of Data in accordance with the established business goals. [1] many people consider data mining as
commonly term of knowledge discovery , while others simply put data mining as a basic steps in the process of
knowledge discovery [2]. It appeared in the late 1980s, which is new areas with a great researching value in the
study of the database, and overlapping subject, combined with artificial intelligence, database technology, pattern
recognition, machine learning, statistics, data visualization, and other fields of theory and technology. As a kind
of technology, the life cycle of data mining is in unclear stage, experts need takes time and energy to research,
develop and propel to be mature gradually, eventually been accepted [3-4]. Data mining is a kind of technology,
which combines the traditional data processing methods with different algorithms, to analyze new data types and
extract knowledge from huge amounts of data. Found in huge amounts of data, there are two kinds of knowledge,
one is on-line analytical processing (OLAP), the other is a data mining (DM). Both are analysis tools based on
data warehouse, but on-line analytical processing appears earlier than the data mining, based on a
multidimensional view, emphasizing the execution efficiency and quick response for user commands; And data
mining is pay attention to the useful model to people hiding in the depths of data, and it is done by automation,
without participation of customers.
2.1 On what data mining
Data mining can examine any type of data and information flow, its difficulty is relative with database type.
(1) Mining for relational database. A relational database is the set of tables; table is composed of attributes group,
depositing large number of tuples. Usually use ER model to represent the connection between the database and
the real. Excavating from a relational database, the trends and data model can be obtained.
For example, the customer's income, age, education level, and other information can be obtained and by
commercial relational database for mining, then making targeted marketing for customer and avoiding fraud, and
shape a company's strategy.
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(2) Mining for data warehouse. Data warehouse is a subject oriented, integrated, non-volatile and time-variant
collection of data, which contains consistent data used in enterprise decision support [5-6]. The data warehouse is
the data environment that can be used as the single integrated source of data for processing information. Data
warehouses deposit aggregated data, which are processed to find hidden patterns and relation to structure
analytical model to classify and forecast.
( 3) Mining for new database. The new database includes spatial database, time database and text database and
multimedia database. These data include spatial data, text, data, image and audio data and streaming data and web
data. The data structure is more complex and dynamic change, more difficult to handle. For example, through the
data mining technology can find the evolution of the object characteristics and trends; streaming data are clustered
and compared to find interesting patterns.
2.2 The evolution process of data mining
1960s, database technology, and information technology has gradually developed from the basic document
processing system to more complex and more powerful database system, such as hierarchical and network
database are typical representative of this era with little data independence and abstraction. 1970s, relational
databases appear, allowing users access to a flexible data access language and interface, OLTP technology make
the relational database technology application gained popularity; Mid-1980s, the rise of a powerful database
system, and put forward many advanced data models. for example expanding the relational model, object-oriented
model, the interpretation of the model, etc. by the end of 80 advanced data model and application oriented
database were developed.After 2000, the ability to store large amounts of data is over the capacity of human
analysis and understanding; there is no suitable tool to help extracting information and knowledge from the data.
The existence of specific patterns and rules can be found by data mining tools in a large amount of data, which
can provide the necessary information for commercial activity, scientific exploration and medical research and
many other areas.Business intelligence (BI) based on data mining has become the new darling of the IT industry.
Currently the data mining has been successfully applied in retail goods basket data analysis, financial risk
prediction, product quality, molecular biology, genetic engineering, discovery of Internet site access patterns, the
information search and classification and many other fields.
2.3 Data Mining Tasks
2.3.1 Forecasts
With assignment to the line attribute or object attribute unknown category data, we use the obtained learning
model to forecast. Classification and regression are two major kinds of prediction model. The former is used to
predict discrete or symbolic value, and the regression is used to predict continuous values. The answer is Y or N
for the question of purchase of goods online. While to forecast future stock prices and trends are regression-based
tasks. Predictive models can determine the benefits and risks of future market, also can predict the earth's
resources consumption.
2.3.2 Description
Summed up the relationship that exists potential data model is a role of authentication and interpretation. Usually
correlation analysis use to describe model with strong corelational characteristics, to extract interesting pattern to
find the correlation in the data. Extract characteristic formulas expressed the general characteristics of the data set
from the data warehouse, Or find other features to distinguish the characteristics of style.[5] For example, feature
extraction and distinction from other cases. While they are commonsense, association rule mining can find many
other interesting interactions, such as 'beer and diaper'. This is the famous market basket analysis, which was once
a secret weapon in Wal-Mart, market basket analysis can help us find stores selling process with affiliated
2.4 Classification of Data Mining
Data mining can be divided into two categories, direct and indirect data mining. The goal of direct data mining is
to use the available data to create model with a description of variables. The goal of indirect data mining is to no
choice of a specific variable, but to establish a relationship of all the variables [5].Classification, estimation and
prediction are direct data mining; Association rule, clustering, description, and visualization are indirect data
International Journal of Business and Social Science
Vol. 4 No. 16; December 2013
Association rules is not known in advance what would be the knowledge obtained, what get after the analysis of
data. Such as customers buy A product with B product. Clustering is grouping on similar records and put the
similar records in a gathering.The difference between clustering and classification is that clustering does not rely
on pre-defined classes, no training set. Description and visualization is a representation of the data mining results.
3. Common method of data mining
3.1 Correlation analysis
Discover useful patient or associated knowledge useful from a large data set. Basic idea express as: W - > B, W
represents the set of attributes, B represent attributes individual, rules simply interpreted if W with true value,
individual B obtains the possibility and trend of true value in the database list[6]. After buying a commodity, how
likely does continue to buy B commodity?
3.2 Decision Tree.
A decision tree composed by a series of nodes and branches, and the nodes link sub-nodes by branches .Nodes
represents attributes considered in the decision-making process, and different attribute values form different
branches [7]. Using the decision tree model to make decisions, you can search from the root to the leaves; leaf
nodes containing decision the results of each scheme.
3.3 Genetic Algorithm
Genetic algorithms are a probabilistic search to find the optimal process. It is generated from random or specific
groups, in accordance with certain rules of operation to continue iterative calculation, such as selection,
reproduction, crossover and mutation, etc., and it is the process of retaining fine varieties, eliminating inferior
varieties, and guiding the search to approaching optimal solution according to each individual's fitness. Genetic
algorithm execution requires two data conversion, the encoding and decoding. Coding is to convert parameters of
search space to chromosome or individuals of genetic space; Decoding is to convert chromosome or individuals
of genetic space to parameters of search space. Genetic algorithm is developed on the basis of simulation of
genetics, to operate directly on the structure objects. It has very good robustness without restriction of derivative
and function [8-9].
3.4 Bayesian Networks
Bayesian network is based on the mathematical model of probabilistic inference, a probabilistic inference is
through some information to obtain the probability of other variables, Bayesian network based on probabilistic
inference is proposed to solve the problem of uncertainty and incompleteness, it has the very advantage of solving
fault caused by complex uncertainty and correlation, widely used in many fields. With N nodes in bayesian
networks can be expressed by BN N  V , E , P , V , E is N nodes with a directed acyclic graph, node Vi  V
(V , V )  E
is abstraction of unit status, observation, personnel operation. Directed edges i j
represents there is a
direct influence or causal relationship between nodes Vi and V j . Vi is V j ’s father node. P represents associated
with each node condition probability distribution, it expresses the nodes associated with the parent node. Using
bayesian network structure and the conditional probability tables, calculate the probability of values of certain
node after giving evidence
3.5 Rough Set Approach
Rough set theory is a mathematical methods to deal with the ambiguity and uncertainty , using the method of
rough set can analyze the decision table, can evaluate the importance of a specific attribute, build property set
reduction, nuclear, and get rid of redundant attributes from the decision table, classification rules arise from the
table of reduction to make decision. The main idea of rough set based on the existing knowledge on the given
problem, through classifying to manage for the actual data, divide the domain of the problem,reduct data under
the premise of retaining critical information, get reduction and nuclear of knowledge, assess the dependencies
between data ,derived classification rules of concept [10].
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3.6 Neural Network
Neural network is a dynamic system with topology structure of directed the graph; it deals with information
through response for the continuous or intermittent input state. Neural network system is made up of large and
simple processing units, through widely connected to each other and formed a complex network of systems.
Although the structure and function of each neuron is very simple, but the behavior of the network system
consisting of a large number of neurons is really colorful and very complicated. The algorithm is suitable for data
clustering, which can make a lot of complex information data is systematic and ordinary, so as to find the inner
relationship between the data through the analogy of time and space.
3.7 Statistical Analysis
Statistical analysis is accurate method of data mining based on statistics and probability theory. For example,
regression analysis and factor analysis, through the modeling of objects, find a conclusion. Usually divided into
the following steps: description to the nature of the analytical data, researching group of data relationship,
building of a model, summary of data and relationship of basis group, explanation the validity of the model, and
finally prediction for the future development. SPSS and SAS are widely used as application software of statistical.
4. Data Mining Applications
Data mining technology is application-oriented. In many areas, the data mining have played a major role,
especially in the banking, insurance, and transportation and retailing, data mining can solve a lot of business
issues, increase business profits and make wise decisions. Retail is not only the first application of data mining
techniques but also important areas. Because the retail industry has accumulated a lot of sales data, such as
customer purchase records, consumer information and service information, etc. Enterprises can use the data to
classify customers, from basic customer groups found gold customer characteristics and consumer intent, and as
far as possible to provide satisfactory products and services for them. Application of data mining software, choose
the appropriate algorithm, knowledge hidden behind the data would be found. The information obtained by data
mining applies in making marketing strategy to guide enterprise decision-making. Retail data Mining main
functions are: market positioning, consumer analysis, forecast sales trends, marketing strategy optimization,
inventory requirements analysis, customer buying pattern recognition, shelf placement assistance, the time to
develop promotional activities, promotional merchandise combination as well as slow-moving and selling goods
of the situation of other commercial activities. Goods basket analysis is the most widely used in data mining, with
product sales, product pricing, promotion activities to reduce business costs and increase profits.
Now, many of popular e-commerce are the application of data mining, product portfolio marketing and product
recommendations, but will provide customers with personalized marketing at the same time, optimization of
website design and product promotion. Secondly in the financial industry, data mining also played a big role.
Insurance industry is a serious information asymmetry, the analysis of the policy-holder can reduce the risk, and it
is only through the mining customs’ history to understand customer behavior effectively, prevent the happening
of insurance fraud. Bank loans, securities heavily depend on data mining. When banks lend, it need find
customers' wage income, education level, credit history and other factors that affect credit, and then decide
whether to loan, set up a model of credit fraud to predict the risks and benefits. Medicine and biotechnology, the
genetic analysis of these data through data mining technology is helpful to research and understand [11]. Data
mining decision inference which has been widely applied in the field of medicine, build medical model aimed at
the study of difficult problems to find the essence of connection and phenomenon, reasoning a new treatment of
the disease.
BES are committed maybe the loss of bank customers through SPSS software to identify the characters of
customers who are likely to leave the bank. Jorge Portugal and his team analyze these dynamic relationships and
build models to develop appropriate adjustment strategy to improve customer satisfaction. Results show that
customer churn rate was reduced by 15% - 15%, profits rose by 10% to 10%. HSBC Bank may have a number of
banks with branches in the same area, resulting in a sustainable competitive to attract and retain potential
customers nearby. In order to maintain a high level of customer acquisition, maintenance of profitability, banks
must expand existing customer relationships, control marketing costs in order to maintain profits and rapid
transfer market, HSBC dig on growing customer data , build predictive modeling to discover opportunities for
cross-selling and tumbling.
International Journal of Business and Social Science
Vol. 4 No. 16; December 2013
Positioned at each customer with the best value, maximize products sales, minimal marketing costs. Just three
years, the bank product sales increased by 50%, the marketing cost reduced by 30%, improve the ability to
establish and carry out real-time marketing strategy.
5. Data Mining Challenges
5.1 Performance of data mining
Large data increases the challenge of algorithms and computing platforms curse of dimensionality is more severe,
computational overhead is also increased. Fundamentally speaking, from large data production, storage,
protection, archiving to safety maintenance of various angles, this is the category of IT management maintenance,
but when data quantity is beyond conventional management scale, the difficulty to maintain management
appeared upward trend.
5.2 The diversification of data types
The structure of data on the web is poor, and most of them is semi-structured or unstructured, since semistructured and unstructured information can't clearly express by the data model, therefore it is necessary to
develop new data mining tools which is suitable for web data in the Web mining. In the field of remote sensing,
scanning the earth via satellite, every day we can get a lot of remote sensing image of the earth's surface, In the
field of remote sensing, scanning the earth via satellite, every day we can get a lot of remote sensing image of the
earth's surface, spatial data mining technology must be used from a large amount of data of each image hidden [12].
5.3 data security
As the issues of cyber attacks become more prominent, the importance of network security audit is increasingly
apparent. Prism that broke out in June is a typical data mining problem of safety, U.S. intelligence agencies attack
and monitor on his country's Internet to obtain confidential data through IT enterprises network to collect data
from countries around the world. Laws alone can not safeguard the interests of a country, how to build an
effective security strategy to protect against unauthorized use of these data will be the focus of future research
6. Data Mining Trends
Data mining is a new kind of intelligent information processing technology. With the rapid development of
information technology, the application in the field of data mining will broaden and deepen ceaselessly, especially
in the military, security, business intelligence applications. Mobile data mining will be the future direction of data.
the powerful Internet capture the science data sets and the social sequence of data set, topology, geometry and
other characteristics, connection graph mining and social network analysis usefully. Data mining is directly facing
massive databases, so data mining algorithm must be efficient and scalable. The mostly current databases are
relational database; the emergence of database of models in future need ensure the ability of processing the type
of data is important. Operators with appropriate participation in data mining can accelerate data mining process.
On the one hand, the interactive interface for users to provide convenience to express requirements and strategies;
On the other hand, interactive interface transform the generated results which are varied to the user, namely data
mining system requires strong interactivity. At the same time, that the research of data mining can lead to illegal
data invasion is a problem to be solved in real life [13]. In China, data mining technology started late, compared
with the United States and other developed countries, there are many shortcomings, for example, almost all our
computers still use Microsoft's operating system, the U.S. intelligence agencies on the implementation of the
network listening and attacks, resulting in a lot of data leakage, we need in data mining, and must continue to
develop technologies to prevent data leakage. In this paper, author hope to provide people who are interested in
some of the data mining work with some guidance and help
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