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

... demonstrates that, on a technical 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 ... combination of techniques to apply in a particular situation depends on the nature of the data mining task, the nature of the available data, and the skills and preferences of the data miner. Data ... the charge of banks into data mining, but other divisions are not far behind. At Wachovia, a large North Carolina-based bank, data mining techniques are used to predict which customers are likely...

Ngày tải lên: 07/04/2014, 11:16

... has an equal number of training data items. After the moving phase and before the load balancing phase starts, each processor has training data item count varying from 0 to Each processor can ... Mathematics from Harvard University and a Ph.D. in Mathematics from Princeton University. PARALLEL FORMULATIONS 261 Mehta, M., Agrawal, R., and Rissaneh, J. 1996. SLIQ: A fast scalable classifier ... details. 5HIHUHQFHV Agrawal, R., Imielinski, T., and Swami, A. 1993. Database mining: A performance perspective. IEEE Transactions Alsabti, K., Ranka, S., and Singh, V. 1997. A one - pass algorithm...

Ngày tải lên: 01/06/2014, 09:18

... subclass hierarchies, property inheritance, and methods and procedures. Temporal Databases, Sequence Databases, and Time-Series Databases A temporal database typically stores relational data that ... fiscal years, academic years, or calendar years. Years may be further decomposed into quarters or months. Spatial Databases and Spatiotemporal Databases Spatial databases contain spatial-related ... data repository, as well as to transient data, such as data streams. Thus the scope of our examination of data repositories will include relational databases, data warehouses, transactional databases,...

Ngày tải lên: 08/08/2014, 18:22

... cleaning on future versions of the same data store. 2.4 Data Integration and Transformation Data mining often requires data integration—the merging of data from multiple data stores. The data may ... you may already have regarding properties of the data. Such knowledge or data about data is referred to as metadata. For example, what are the domain and data type of each attribute? What are ... inconsistent. Data preprocessing includes data cleaning, data integration, data transformation, and data reduction. Descriptive data summarization provides the analytical foundation for data pre- processing....

Ngày tải lên: 08/08/2014, 18:22

... transaction databases, relational databases,spatial databases, text databases, time-series databases, flat files, data warehouses, and so on. On-line analytical mining (OLAM) (also called OLAP mining) ... Metadata Repository Metadata are data about data. When used in a data warehouse, metadata are the data that define warehouse objects. Figure 3.12 showed a metadata repository within the bottom tier ... data mining technology. 3.5.1 Data Warehouse Usage Data warehouses and data marts are used in a wide range of applications. Business executives use the data in data warehouses and data marts to...

Ngày tải lên: 08/08/2014, 18:22

... rules are commonly 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. ... database from a relatively low conceptual level to higher conceptual levels. Data generalization approaches include data cube–based data aggregation and attribute- oriented induction. From a data ... , a 100 }, it has to generate at least 2 100 −1 ≈10 30 candidates in total. It may need to repeatedly scan the database and check a large set of candidates by pattern matching. It is costly to...

Ngày tải lên: 08/08/2014, 18:22

... 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 required. Earlier ... Chapter 6 Classification and Prediction analysis to help guess whether a customer with a given profile will buy a new computer. A medical researcher wants to analyze breast cancer data in order to ... a prede- fined class as determined by another database attribute called the class label attribute. The class label attribute is discrete-valued and unordered. It is categorical in that each value...

Ngày tải lên: 08/08/2014, 18:22

... typi- cally assume that the data are memory resident a limitation to data mining on large databases. Several scalable algorithms, such as SLIQ, SPRINT, and RainForest, have been proposed to address ... 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 transformation, ... neighbors and restricts the search to subgraphs that are smaller than the original graph. While CLARA draws a sample of nodes at the beginning of a search, CLARANS dynamically draws a random sample...

Ngày tải lên: 08/08/2014, 18:22

... concepts and techniques of data mining. The techniques studied, however, were for simple and structured data sets, such as data in relational databases, transactional databases, and data warehouses. ... semantic information, such as time-series streams, spatiotemporal data streams, and video and audio data streams. 8.2 Mining Time-Series Data “What is a time-series database?” A time-series database ... evolving data streams was pro- posed by Aggarwal, Han, Wang, and Yu [AHWY03]. A framework for projected cluster- ing of high-dimensional data streams was proposed by Aggarwal, Han, Wang, and Yu [AHWY0 4a] . A...

Ngày tải lên: 08/08/2014, 18:22

... unstructured data, ranging from text and Web data to multimedia data- bases. It is useful for data integration tasks in many domains. Metadata mining can be used for schema mapping (where, say, the attribute ... to protein structures. In chemistry, we can search for subgraphs representing chemical substructures. 9. Metadata mining. Metadata are data about data. Metadata provide semi-structured data about ... data streams. The k-median- based STREAM algorithm was proposed by Guha, Mishra, Motwani, and O’Callaghan [GMMO00] and by O’Callaghan, Mishra, Meyerson, et al. [OMM + 02]. Aggarwal, Han, Wang, and...

Ngày tải lên: 08/08/2014, 18:22

... spatial and nonspatial data in support of spatial data mining and spatial -data- related decision-making processes. Let’s look at the following example. Example 10.5 Spatial data cube and spatial ... large multimedia databases,multimedia data cubes can be designed and constructed in a manner similar to that for traditional data cubes from relational data. A multimedia data cube can contain ... we can integrate spatial data to construct a data warehouse that facilitates spatial data mining. A spatial data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of...

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