Data WareHouseing & Mining

Data WareHouseing & Mining

ABSTRACT

            One may claim that the exponential growth in the amount of data provides great opportunities for data mining.  In many real world applications, the number of sources over which this information is fragmented grows at an even faster rate, resulting in barriers to widespread application of data mining. suppor. Data mining is the “ non trivial  process  of identifying  valid, novel , potentially useful, and ultimately understandable patterns in Ind.  Data mining is concerned with the analysis of  data and the use of software technique for finding patterns and regularities  in sets of data. Data mining potential can be enhanced  if the appropriate  data has been collected  and stored in data warehouse

INTRODUCTION DATA MINING

                   Data mining  or knowledge discovery in data bases is  the nontrivial extraction of implicit, previously  unknown and potentially  useful information from  the data. This encompasses a number of technical approaches, such as clustering , data summarization, finding dependency networks, classification  analyzing changes , and detecting anomalies. Data mining search for the  relationship and global patterns that exists in large databases  byt are hidden among of data ,such as the  relationship between  patient data and medical diagnosis. The relationship represents valuable knowledge  about the databases, and objects in the database, it the database is a faithful mirror  of the real word  registered by the database. If refers to  using a variety of techniques to identify nuggets  of  information  or decision  making knowledge in the  database and  extracting these  in such a way  that they  can be put  to use  in areas such as  decision support , prediction ,forecasting  and estimation . In particular , finding  associations between items in a database of customer transaction. Market basket analysis technique used to group items together. A rule  may contain  more than one ,item in the  antecedent and the consequent of the rule. In this paper . we concentrate  on finding association, but  with different slant (i.e) by using partition  algorithm. In the next section , we review  the basis concepts of association rule.

 ADVANTAGES

         -  Data warehouse are free from the restrictions of the transactional      environment      

             There is   an increased efficiency in query processing.

-          Artificial intelligence techniques, which may include genetic algorithm And    neural networks, are used classification and    are employed to discover knowledge from  the data warehouse  that may be unexpected or Difficult to specify queries.

APPLICATONS

                   Data warehousing  can be  a key differentiator  in many industries . At present , some  of the most popular  Data warehouse application include:

·         Sales and marketing analysis  across all industries.

·         Inventory turn  and product tracking  in manufacturing.

·         Category management ,vendor analysis , and marketing , program effectiveness analysis in retail

·         Profitability  analysis or risk  assessment in banking.

·         Claims analysis or fraud detection in insurance.

           Data mining has many and varied fields of applications such as:

a.        Retail/Marketing

·         Identify  buying patterns from customers

·         Find  associations  among customers demographic characteristics.

·         Predict response to mailing campaigns.

·         Market basket analysis.

b.        Banking

·         Detect pattern  of fraudulent credit card use

·         Identify ‘loyal’ customer.

·         Determinine credit card  spending  by customer groups

·         Find hidden correlation  between different financial indicators.

c.         Medicine

·         Characterize patient behavior  to protect  office visits

·         Identify successful  medical therapies  for different  illness.

d.  Transportation

·         Determine  the distribution schedule among outlets

·         Analyze loading patterns

e.       Insurance and Health Care

·         Claim analysis – i.e which medical procedure are claimed

Together.

·         Predict which customer will buy new polices.

·         Identify behavior pattern of risky customers

·         Identify fraudulent behavior

            *HOW DATA WAREHOUSE& DATAMINING IS USEFUL IN GOVERNMENT

                   A large number of data warehouse  can be identified  from existing data sources  with in the central government  ministers. Let us examine potential areas on which data warehouse may be developed  and also in future.

      CECNSUS DATA, AGRICULTURE, RURAL DEVELOPMENT, HEALTH PLANNING,    EDUCATION,  COMMERCE AND TRADE.

   OTHER SECTORS:

                   Tourism, Programme implementation, Revenue, Economic affairs, Audit and Accounts.

CRITICAL ISSUES

                   Data ware housing  helps  business makes informed decisions. But there are  a few critiacal issues  that must be  faced a head  on while designing and implementation a data warehouse. These issues are as follows.

·         Capacity planning

·         Security  backup and recovery

·         Service level agreement

·         Performance tuning

·         Testing

·         Implementation obstacle

CONCLUSION:                  

            .          Data mining tool can enhance inference process. Speed up design cycle, but con not be substitute for statistical and domain expertise. Data mining allows for the creation of a self learning organization.

              So the future of data warehouse lies in their accessibility from the internet. Successful  implementation of  a data warehouse and  data mining requires  a high performance; scalable combination of hardware and software which can integrate easily within existing system, so customer can use  data warehouse  to improve their decision –making—and their competitive advantage