... 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...
... Statistical Data Mining 666
11.3.3 Visual and Audio Data Mining 667
11.3.4 Data Mining and Collaborative Filtering 670
11.4 Social Impacts of Data Mining 675
11.4.1 Ubiquitous and Invisible Data Mining ... object-relational databases and
specific application-oriented databases, such as spatial databases, time-series databases,
text databases, and multimedia databa...
... 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
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 go over each tra...
... 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 ... itemset
stream mining; the Hoeffding tree, VFDT, and CVFDT algorithms for stream data
classification; and the STREAM and CluStream algorithms for stream data...
... 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 ... inbroadcast data
streams, and in personal and professional databases. This amount is rapidly growing.
There are great demands for effective content-base...
... 97
2.7
Summary
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 preprocessing
includes data cleaning, ... approximation of the original data.
PCA is computationally inexpensive, can be applied to ordered and unordered
attributes, and can handle sparse data and...
... processing, and data
mining. We also introduce on-line analytical mining (OLAM), a powerful paradigm that
integrates OLAP with data mining technology.
3.5.1 Data Warehouse Usage
Data warehouses and data ... 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 sec...
... to as training tuples and are selected from the database under analysis. In the
context of classification, data tuples can be referred to as samples, examples, instances,
data points, or objects.
2
Because ... Decision Tree Induction 309
Figure 6 .10 The use of data structures to hold aggregate information regarding the training data (such as
these AVC-sets describing the data of...
... 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...
... 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 relational
databases, transactional databases, and data warehouses. The growth of data in various
complex forms (e.g....