Data Mining_ Concepts and Techniques - Jiawei Han [16]
A data warehouse is usually modeled by a multidimensional data structure, called a data cube, in which each dimension corresponds to an attribute or a set of attributes in the schema, and each cell stores the value of some aggregate measure such as count or sum(sales_amount). A data cube provides a multidimensional view of data and allows the precomputation and fast access of summarized data.
A data cube for AllElectronics
A data cube for summarized sales data of AllElectronics is presented in Figure 1.7(a). The cube has three dimensions: address (with city values Chicago, New York, Toronto, Vancouver), time (with quarter values Q1, Q2, Q3, Q4), and item (with item type values home entertainment, computer, phone, security). The aggregate value stored in each cell of the cube is sales_amount (in thousands). For example, the total sales for the first quarter, Q1, for the items related to security systems in Vancouver is $400,000, as stored in cell 〈Vancouver, Q1, security〉. Additional cubes may be used to store aggregate sums over each dimension, corresponding to the aggregate values obtained using different SQL group-bys (e.g., the total sales amount per city and quarter, or per city and item, or per quarter and item, or per each individual dimension).
Figure 1.7 A multidimensional data cube, commonly used for data warehousing, (a) showing summarized data for AllElectronics and (b) showing summarized data resulting fromdrill-down and roll-up operations on the cube in (a). For improved readability, only some of the cube cell values are shown.
By providing multidimensional data views and the precomputation of summarized data, data warehouse systems can provide inherent support for OLAP. Online analytical processing operations make use of background knowledge regarding the domain of the data being studied to allow the presentation of data at different levels of abstraction. Such operations accommodate different user viewpoints. Examples of OLAP operations include drill-down and roll-up, which allow the user to view the data at differing degrees of summarization, as illustrated in Figure 1.7(b). For instance, we can drill down on sales data summarized by quarter to see data summarized by month. Similarly, we can roll up on sales data summarized by city to view data summarized by country.
Although data warehouse tools help support data analysis, additional tools for data mining are often needed for in-depth analysis. Multidimensional data mining (also called exploratory multidimensional data mining) performs data mining in multidimensional space in an OLAP style. That is, it allows the exploration of multiple combinations of dimensions at varying levels of granularity in data mining, and thus has greater potential for discovering interesting patterns representing knowledge. An overview of data warehouse and OLAP technology is provided in Chapter 4. Advanced issues regarding data cube computation and multidimensional data mining are discussed in Chapter 5.
1.3.3. Transactional Data
In general, each record in a transactional database captures a transaction, such as a customer's purchase, a flight booking, or a user's clicks on a web page. A transaction typically includes a unique transaction identity number (trans_ID) and a list of the items making up the transaction, such as the items purchased in the transaction. A transactional database may have additional tables, which contain other information related to the transactions, such as item description, information about the salesperson or the branch, and so on.
A transactional database for AllElectronics
Transactions can be stored in a table, with one record per transaction. A fragment of a transactional database for