27-06-2012, 11:52 AM
Data mining-An Overview
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Introduction
This Chapter provides a brief introduction to the basic concepts of data mining, incremental data mining and data clustering.Data clustering is the core of this chapter. Data mining is a very important tool use in various research fields now a day. All aspects of data mining and one of its important tools ‘Clustering’ are clearly discussed in this chapter.
Data mining-An Overview
“Data rich but information is poor” – to reduce this disadvantage data mining comes into the picture.Data mining is a young and promising field to extract useful knowledge from large amount of data store. That’s why it is also called “knowledge discovery from data”.It can also refer as mining knowledge from large data just like mining gold from rocks in gold mining [4].Data mining involves the use of sophisticated data analysis tools to discoverpreviously unknown, valid patterns and relationships in large data sets.These tools can include statistical models, mathematical algorithms, and machine learning methods. The following ‘Figure 1.1’ shows how the data mining actually work.
Limitations of Data mining
While data mining products can be very powerful tools, they are not self-sufficientapplications. To be successful, data mining requires skilled technical and analytical specialists who can structure the analysis and interpret the output that is created. Consequently, the limitations of data mining are primarily data or personnel related, rather than technology-related.Although data mining can help reveal patterns and relationships, it does not tell the user the value or significance of these patterns. These types of determinations must be made by the user.
Applications of Data mining
Data mining is emerging as one of the key features of many homeland security initiatives.Data mining is becoming increasingly common in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing commonly use data mining to reduce costs, enhance research, and increase sales. In the public sector, data mining applications initially were used as a means to detect fraud and waste, but have grown to also be used for purposes such as measuring and improving program performance.
Incremental Data Mining
‘Incremental’ means changes in the existing database i.e. insertion of new data into the database or deletion of old data from the database. This is sometimes called ‘% of delta change in the database’. This is a very important issue now a day. Because at present most of the databases are dynamic in nature. So, we need to develop some new data mining techniques (algorithms) which can handle this dynamic feature of the database efficiently and effectively.The objective of incremental data mining algorithms is to minimize the scanning and calculation effort for newly added records. Here we improve the efficiency of newly added record updating problem. These are the few prime factors that cause to apply the incremental Mining.