data mining techniques and applications u2013 a decade review from 2000 to 2011
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management - Second Edition
... 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...
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high performance data mining scaling algorithms, applications, and systems
... 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. 5HIHUHQFHVAgrawal, 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...
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Data Mining Concepts and Techniques phần 1 potx
... 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 intoquarters or months.Spatial Databases and Spatiotemporal DatabasesSpatial databases contain spatial-related ... data repository, as well as to transient data, such as data streams. Thus the scope of ourexamination of data repositories will include relational databases, data warehouses,transactional databases,...
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Data Mining Concepts and Techniques phần 2 ppsx
... cleaning on future versions of thesame 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 ofeach attribute? What are ... inconsistent. Data preprocessingincludes data cleaning, data integration, data transformation, and data reduction.Descriptive data summarization provides the analytical foundation for data pre-processing....
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Data Mining Concepts and Techniques phần 3 docx
... 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 RepositoryMetadata are data about data. When used in a data warehouse, metadata are the data thatdefine warehouse objects. Figure 3.12 showed a metadata repository within the bottomtier ... data mining technology.3.5.1 Data Warehouse Usage Data warehouses and data marts are used in a wide range of applications. Businessexecutives use the data in data warehouses and data marts to...
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Data Mining Concepts and Techniques phần 4 potx
... rules arecommonly mined from transactional data. Suppose, however, that rather than using a transactional database, sales and relatedinformation 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 2100−1 ≈1030candidates in total.It may need to repeatedly scan the database and check a large set of candidates by patternmatching. It is costly to...
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Data Mining Concepts and Techniques phần 5 ppt
... due to swapping of the training tuples in and out of main and cache memories. More scalable approaches, capable of handlingtraining data that are too large to fit in memory, are required. Earlier ... Chapter 6 Classification and Predictionanalysis 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 eachvalue...
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Data Mining Concepts and Techniques phần 6 ppt
... typi-cally assume that the data are memory resident a limitation to data mining on largedatabases. Several scalable algorithms, such as SLIQ, SPRINT, and RainForest, havebeen proposed to address ... preparation for classification and prediction can involve data cleaning to reduce noise or handle missing values, relevance analysis to removeirrelevant or redundant attributes, and data transformation, ... neighbors and restricts the search to subgraphs that are smallerthan the original graph. While CLARA draws a sample of nodes at the beginning of a search, CLARANS dynamically draws a random sample...
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Data Mining Concepts and Techniques phần 7 ppsx
... simple and structured data sets, such as data in relationaldatabases, transactional databases, and data warehouses. The growth of data in variouscomplex forms (e.g., semi-structured and unstructured, ... telecommu-nications data, transaction data from the retail industry, and data from electric powergrids. Traditional OLAP and data mining methods typically require multiple scans ofthe data and are therefore ... 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...
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Data Mining Concepts and Techniques phần 8 potx
... we can search for subgraphs representing chemical 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 integration tasks in many domains. Metadata mining canbe used for schema mapping (where, say, the attribute customerid from ... time-series data streams. MAIDS (Mining Alarming Incidents from Data Streams), a stream data mining system built on top of such a stream data cube, was developed by Cai, Clutter, Pape, et al.[CCP+04].For...
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Data Mining Concepts and Techniques phần 9 pot
... 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 10.5Spatial 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 integratespatial data to construct a data warehouse that facilitates spatial data mining. A spatial data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collectionof...
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