Data Mining Concepts and Techniques phần 10 pot

... constraint-based mining) , the integration of data mining with data warehousing and database systems,the standardization of data mining languages, visualization methods, and new meth-ods for handling ... complex data types. Other trends include biological data mining, mining software bugs, Web mining, distributed and real-time mining, graph mining, social network analysis, multirelational and multidatabase ... multiple data mining systems and functions, and promote the education and use of data mining systems in industry and society.Recent efforts in this direction include Microsoft’s OLE DB for Data Mining...

Data Mining Concepts and Techniques phần 1 potx

... Statistical Data Mining 66611.3.3 Visual and Audio Data Mining 66711.3.4 Data Mining and Collaborative Filtering 67011.4 Social Impacts of Data Mining 67511.4.1 Ubiquitous and Invisible Data Mining ... object-relational databases and speciﬁc application-oriented databases, such as spatial databases, time-series databases,text databases, and multimedia databases. The challenges and techniques of mining ... Reference Data in Enterprise Databases: Binding Corporate Data to the Wider WorldMalcolm Chisholm Data Mining: Concepts and Techniques Jiawei Han and Micheline KamberUnderstanding SQL and Java...

Data Mining Concepts and Techniques phần 4 potx

... 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. Descriptive data mining ... least 2 100 −1 10 30candidates in total.It may need to repeatedly scan the database and check a large set of candidates by patternmatching. It is costly to go over each transaction in the database ... d9, d 10 ), and (3) (d1, d2, c3, d4, , d9, d 10 ), wherea1= d1, b2= d2, and c3= d3. The measure of the cube is count.220 Chapter 4 Data Cube Computation and Data Generalization(a)...

Data Mining Concepts and Techniques phần 8 potx

... 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 ... itemsetstream mining; the Hoeffding tree, VFDT, and CVFDT algorithms for stream data classiﬁcation; and the STREAM and CluStream algorithms for stream data clustering.A time-series database consists ... of sequence data include cus-tomer shopping sequences, Web clickstreams, and biological sequences.548 Chapter 9 Graph Mining, Social Network Analysis, and Multirelational Data Mining graph....

Data Mining Concepts and Techniques phần 9 pot

... multimedia data mining focuses on image data mining. Mining text data and mining the World Wide Web are studied in the two subsequent638 Chapter 10 Mining Object, Spatial, Multimedia, Text, and Web Data where ... inbroadcast data streams, and in personal and professional databases. This amount is rapidly growing.There are great demands for effective content-based retrieval and data mining methodsfor audio and ... closely linked to imageanalysis 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 use of the...

Data Mining Concepts and Techniques phần 2 ppsx

... 972.7Summary Data preprocessing is an important issue for both data warehousing and data mining, as real-world data tend to be incomplete, noisy, and inconsistent. Data preprocessingincludes data cleaning, ... approximation of the original data. PCA is computationally inexpensive, can be applied to ordered and unorderedattributes, and can handle sparse data and skewed data. Multidimensional data of more than ... (inclusive).2.3 Data Cleaning 652.3.3 Data Cleaning as a ProcessMissing values, noise, and inconsistencies contribute to inaccurate data. So far, we havelooked at techniques for handling missing data and...

Data Mining Concepts and Techniques phần 3 docx

... processing, and data mining. We also introduce on-line analytical mining (OLAM), a powerful paradigm thatintegrates OLAP with data mining technology.3.5.1 Data Warehouse Usage Data warehouses and data ... Warehouse and OLAP Technology: An Overview3.5From Data Warehousing to Data Mining “How do data warehousing and OLAP relate to data mining? ” In this section, we study theusage of data warehousing ... Chapter 3 Data Warehouse and OLAP Technology: An Overview data by OLAP operations), and data mining (which supports knowledge discovery).OLAP-based data mining is referred to as OLAP mining, or...

Data Mining Concepts and Techniques phần 5 ppt

... to as training tuples and are selected from the database under analysis. In thecontext of classiﬁcation, data tuples can be referred to as samples, examples, instances, data points, or objects.2Because ... Decision Tree Induction 309Figure 6 .10 The use of data structures to hold aggregate information regarding the training data (such asthese AVC-sets describing the data of Table 6.1) are one approach ... scalability.While both SLIQandSPRINThandle disk-resident data sets thatare too large to ﬁt intomemory, the scalabilityof SLIQ islimited by the useof its memory-residentdatastructure.SPRINT removes...

Data Mining Concepts and Techniques phần 6 ppt

... 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 removeirrelevant or redundant attributes, and data ... the data. Early decision tree algorithms typi-cally assume that the data are memory resident—a limitation to data mining on largedatabases. Several scalable algorithms, such as SLIQ, SPRINT, and...

Data Mining Concepts and Techniques phần 7 ppsx

... efﬁciently.8 Mining Stream, Time-Series, and Sequence Data Our previous chapters introduced the basic concepts and techniques of data mining. The techniques studied, however, were for simple and structured ... 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, spatial and ... 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...

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