21-04-2011, 09:21 AM
presented by
Vikrant M. Kale
Abhijeet V. Thakare
[attachment=12516]
1Abstract:
Data mining, the extraction of hidden predictive information from large database, is a powerful new technology with great potential to help Companies focus on the most important information in their data warehouses.Data Mining tools predicts future trends and behaviors, allowing business to make proactive knowledge-driven decisions.The automated, prospective analysis offered by data mining move beyond the analysis of past events provided by retrospective tools typical of decision support systems.Data Mining tools can answer business questions that traditionally were too time consuming to resolve.Data Mining techniques can be implemented rapidly onn existing software and hardware platforms to enhance the value of existing information resources,and can be integrated with new products and system as they are brought online.
Analyzing data can provide further knowledge about a business by going beyond the data explicitly stored to derive knowledge about the business.
2.Introduction:
What is Data Mining?
The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format.This accumulation of data has taken place at an expensive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-scale or remote sensing devices has contributed to this explosion of available data.
Information is at the heart of business operations and that is used by decision makers to make use of data stored to gain valuable insights into the business.Database Management system gave access to data stored but this was only a small part of what could be gained from the data .Traditional online transaction processing systems, OLTPs, are good at putting data into database quickly, safely and efficiently but are not good at delievering meaningful analysis in return.Analyzing data can provide further knowledge about a business by going beyond the data explicitely stored to derive knowledge about the business.This is where Data Mining or Knowledge Discovery in Databases (KDD) has obvious benefits for any enterprise.
3.Data Mining Background :
Data Mining has drawn on a number of fields such as inductive learning, machine learning, statistics, etc.
Inductive Learning:
Induction is the inference of information from data and inductive learning is the model building process where the environment i.e. database is analyzed with a view to finding patterns. Similar objects are grouped in classes and rules formulated where by it is possible to predict the class of unseen objects. This process of classification identifies classes such that each class has a unique pattern of values, which forms the class description. The nature of the environment is dynamic hence the model must be adaptive i.e. should be able learn.
Inductive learning where the system infers knowledge itself from observing its environment has two main strategies :
* Supervised learning
* Unsupervised learning
Statistics:
Statistics has a solid theoretical foundation but the results from statistics can be overwhelming and difficult to interpret, as they require user guidance as to where and how to analyze the data .Data Mining however allows the expert’s knowledge of the data and the advanced analysis techniques of the computer to work together. For example statistical induction is something like the average rate of failure of machines.
Machine Learning :
Machine learning is the automation of a learning process and learning is tantamount to the construction of rules based on observations of environmental states and transitions. This is a broad field, which includes not only learning from examples, but also reinforcement learning, learning with teacher, etc.
Some Of The Definitions Of Data Mining are :
Data Mining achieves different technical approaches , such as clustering, data summarization, learning classification rules, finding dependency networks, analyzing and detecting anomalies.
Data Mining is the search for the relationships and global patterns that exists in large databases but are ‘hidden’ among the vast amount of data, such as a relationship between patient data and their medical diagnosis. This relationship between patient data and their medical diagnosis. These relationships represent valuable knowledge about the database and the objects in the database.
Basically data mining is concerned with the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. It is the computer which is responsible for finding the patterns by identifying the underlying rules and features in the data.
4. Stages/Process in Data Mining
The following diagram summarizes some of the stages/processes identified in Data Mining and Knowledge Discovery.
Vikrant M. Kale
Abhijeet V. Thakare
[attachment=12516]
1Abstract:
Data mining, the extraction of hidden predictive information from large database, is a powerful new technology with great potential to help Companies focus on the most important information in their data warehouses.Data Mining tools predicts future trends and behaviors, allowing business to make proactive knowledge-driven decisions.The automated, prospective analysis offered by data mining move beyond the analysis of past events provided by retrospective tools typical of decision support systems.Data Mining tools can answer business questions that traditionally were too time consuming to resolve.Data Mining techniques can be implemented rapidly onn existing software and hardware platforms to enhance the value of existing information resources,and can be integrated with new products and system as they are brought online.
Analyzing data can provide further knowledge about a business by going beyond the data explicitly stored to derive knowledge about the business.
2.Introduction:
What is Data Mining?
The past two decades has seen a dramatic increase in the amount of information or data being stored in electronic format.This accumulation of data has taken place at an expensive rate. It has been estimated that the amount of information in the world doubles every 20 months and the size and number of databases are increasing even faster. The increase in use of electronic data gathering devices such as point-of-scale or remote sensing devices has contributed to this explosion of available data.
Information is at the heart of business operations and that is used by decision makers to make use of data stored to gain valuable insights into the business.Database Management system gave access to data stored but this was only a small part of what could be gained from the data .Traditional online transaction processing systems, OLTPs, are good at putting data into database quickly, safely and efficiently but are not good at delievering meaningful analysis in return.Analyzing data can provide further knowledge about a business by going beyond the data explicitely stored to derive knowledge about the business.This is where Data Mining or Knowledge Discovery in Databases (KDD) has obvious benefits for any enterprise.
3.Data Mining Background :
Data Mining has drawn on a number of fields such as inductive learning, machine learning, statistics, etc.
Inductive Learning:
Induction is the inference of information from data and inductive learning is the model building process where the environment i.e. database is analyzed with a view to finding patterns. Similar objects are grouped in classes and rules formulated where by it is possible to predict the class of unseen objects. This process of classification identifies classes such that each class has a unique pattern of values, which forms the class description. The nature of the environment is dynamic hence the model must be adaptive i.e. should be able learn.
Inductive learning where the system infers knowledge itself from observing its environment has two main strategies :
* Supervised learning
* Unsupervised learning
Statistics:
Statistics has a solid theoretical foundation but the results from statistics can be overwhelming and difficult to interpret, as they require user guidance as to where and how to analyze the data .Data Mining however allows the expert’s knowledge of the data and the advanced analysis techniques of the computer to work together. For example statistical induction is something like the average rate of failure of machines.
Machine Learning :
Machine learning is the automation of a learning process and learning is tantamount to the construction of rules based on observations of environmental states and transitions. This is a broad field, which includes not only learning from examples, but also reinforcement learning, learning with teacher, etc.
Some Of The Definitions Of Data Mining are :
Data Mining achieves different technical approaches , such as clustering, data summarization, learning classification rules, finding dependency networks, analyzing and detecting anomalies.
Data Mining is the search for the relationships and global patterns that exists in large databases but are ‘hidden’ among the vast amount of data, such as a relationship between patient data and their medical diagnosis. This relationship between patient data and their medical diagnosis. These relationships represent valuable knowledge about the database and the objects in the database.
Basically data mining is concerned with the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. It is the computer which is responsible for finding the patterns by identifying the underlying rules and features in the data.
4. Stages/Process in Data Mining
The following diagram summarizes some of the stages/processes identified in Data Mining and Knowledge Discovery.
