data mining techniques and applications–a decade review from 2000 to 2011

... level, the data mining effort is working and the data is reasonably accurate. This can be quite comforting. If the data and the data mining techniques applied to it are powerful enough to discover ... used to sort a list of customers from most to least loyal or most to least likely to respond or most to least likely to default on a loan. The data mining process is sometimes referred to as ... of Data Mining 33 Table 2.1 Data Mining Differs from Typical Operational Business Processes TYPICAL OPERATIONAL SYSTEM DATA MINING SYSTEM Operations and reports on Analysis on historical data...

Ngày tải lên: 07/04/2014, 11:16

... Sutiwaraphun, J., To, H.W., and Yang, D. Large scale data mining: Challenges and responses. Proc. of the Third Int’l Conference on Knowledge Discovery and Data Mining. Goil, S., Alum, S., and Ranka, ... performance and wide area data mining systems for over ten years. More recently, he has worked on standards and testbeds for data mining. He has an AB in Mathematics from Harvard University and a ... start - up, developing data mining technologies for application to targeted email marketing. Prior to this, he was a researcher at Hitachi’s data mining research labs. He did his B. Tech. from Indian...

Ngày tải lên: 01/06/2014, 09:18

... chapter. Links to data mining data sets and software. We will provide a set of links to data mining data sets and sites containing interesting data mining software pack- ages, such as IlliMine from the ... Cattell and Douglas K. Barry Data on the Web: From Relations to Semistructured Data and XML Serge Abiteboul, Peter Buneman, and Dan Suciu Data Mining: Practical Machine Learning Tools and Techniques ... Reference Data in Enterprise Databases: Binding Corporate Data to the Wider World Malcolm Chisholm Data Mining: Concepts and Techniques Jiawei Han and Micheline Kamber Understanding SQL and Java Together:...

Ngày tải lên: 08/08/2014, 18:22

... and Transformation Data mining often requires data integration—the merging of data from multiple data stores. The data may also need to be transformed into forms appropriate for mining. This section ... the data. These tools rely on parsing and fuzzy matching techniques when cleaning data from multiple sources. Data auditing tools find discrepancies by analyzing the data to discover rules and ... discrepancy detection and data transformation. Data integration combines data from multiple sources to form a coherent data store. Metadata, correlation analysis, data conflict detection, and the resolution...

Ngày tải lên: 08/08/2014, 18:22

... tools for data warehousing can be categorized into access and retrieval tools, database reporting tools, data analysis tools, and data mining tools. Business users need to have the means to know ... Warehouse and OLAP Technology: An Overview 3.5 From Data Warehousing to Data Mining “How do data warehousing and OLAP relate to data mining? ” In this section, we study the usage of data warehousing ... reasons: High quality of data in data warehouses: Most data mining tools need to work on integrated, consistent, and cleaned data, which requires costly data clean- ing, data integration, and data transformation...

Ngày tải lên: 08/08/2014, 18:22

... data mining. Descriptive data mining describes data in a concise and summarative manner and presents interesting general properties of the data. This is different from predic- tive data mining, ... mined from transactional data. Suppose, however, that rather than using a transactional database, sales and related information are stored in a relational database or data warehouse. Such data stores ... levels. Data generalization approaches include data cube–based data aggregation and attribute- oriented induction. From a data analysis point of view, data generalization is a form of descriptive data mining. ...

Ngày tải lên: 08/08/2014, 18:22

... resorting. SPRINT was designed to be easily parallelized, further contributing to its scalability. While both SLIQandSPRINThandle disk-resident data sets thatare too large to fit into memory, the scalabilityof ... becomes inefficient due to swapping of the training tuples in and out of main and cache memories. More scalable approaches, capable of handling training data that are too large to fit in memory, are ... initialized to small random num- bers (e.g., ranging from −1.0 to 1.0, or −0.5 to 0.5). Each unit has a bias associated with it, as explained below. The biases are similarly initialized to small random...

Ngày tải lên: 08/08/2014, 18:22

... functions (Hanson and Burr [HB88]), dynamic adjustment of the network topology (Me´zard and Nadal [MN89], Fahlman and Lebiere [FL90], Le Cun, Denker, and Solla [LDS90], and Harp, Samad, and Guha [HSG90] ), and ... data in preparation for classification and prediction can involve data cleaning to reduce noise or handle missing values, relevance analysis to remove irrelevant or redundant attributes, and data ... difficult to control. Ability to deal with noisy data: Most real-world databases contain outliers or missing, unknown, or erroneous data. Some clustering algorithms are sensitive to such data and may...

Ngày tải lên: 08/08/2014, 18:22

... telecommu- nications data, transaction data from the retail industry, and data from electric power grids. Traditional OLAP and data mining methods typically require multiple scans of the data and are therefore ... simple and structured data sets, such as data in relational databases, transactional databases, and data warehouses. The growth of data in various complex forms (e.g., semi-structured and unstructured, ... be extended to mine such patterns efficiently. 8 Mining Stream, Time-Series, and Sequence Data Our previous chapters introduced the basic concepts and techniques of data mining. The techniques studied,...

Ngày tải lên: 08/08/2014, 18:22

... substructures. 9. Metadata mining. Metadata are data about data. Metadata provide semi-structured data about unstructured data, ranging from text and Web data to multimedia data- bases. It is useful for data ... what window size to use, and CpG islands tend to vary in length. What if, instead, we merge the two Markov chains from above (for CpG islands and non-CpG islands, respectively) and add transition ... domains. Metadata mining can be used for schema mapping (where, say, the attribute customer id from one database is mapped to cust number from another database because they both refer to the 9.2...

Ngày tải lên: 08/08/2014, 18:22

... are closely linked to image analysis and scientific data mining, and thus many image analysis techniques and scien- tific data analysis methods can be applied to image data mining. The popular ... data, and computer tomography. It is important to explore datamining inraster or image databases.Methods for mining raster and image data are examined in the following section regarding the mining ... multimedia data mining focuses on image data mining. Mining text data and mining the World Wide Web are studied in the two subsequent 638 Chapter 10 Mining Object, Spatial, Multimedia, Text, and Web Data where...

Ngày tải lên: 08/08/2014, 18:22

Ngày tải lên: 08/08/2014, 18:22

Ngày tải lên: 14/08/2014, 11:21

Ngày tải lên: 14/08/2014, 11:21

Ngày tải lên: 14/08/2014, 11:21

Ngày tải lên: 14/08/2014, 11:21

Ngày tải lên: 14/08/2014, 11:21

Ngày tải lên: 14/08/2014, 11:21