Data Mining_ Concepts and Techniques - Jiawei Han [106]
This leads us to the notion of concept description, which is a form of data generalization. A concept typically refers to a data collection such as frequent_buyers, graduate_students, and so on. As a data mining task, concept description is not a simple enumeration of the data. Instead, concept description generates descriptions for data characterization and comparison. It is sometimes called class description when the concept to be described refers to a class of objects. Characterization provides a concise and succinct summarization of the given data collection, while concept or class comparison (also known as discrimination ) provides descriptions comparing two or more data collections.
Up to this point, we have studied data cube (or OLAP) approaches to concept description using multidimensional, multilevel data generalization in data warehouses. “Is data cube technology sufficient to accomplish all kinds of concept description tasks for large data sets?” Consider the following cases.
■ Complex data types and aggregation: Data warehouses and OLAP tools are based on a multidimensional data model that views data in the form of a data cube, consisting of dimensions (or attributes) and measures (aggregate functions). However, many current OLAP systems confine dimensions to non-numeric data and measures to numeric data. In reality, the database can include attributes of various data types, including numeric, non-numeric, spatial, text, or image, which ideally should be included in the concept description.
Furthermore, the aggregation of attributes in a database may include sophisticated data types such as the collection of non-numeric data, the merging of spatial regions, the composition of images, the integration of texts, and the grouping of object pointers. Therefore, OLAP, with its restrictions on the possible dimension and measure types, represents a simplified model for data analysis. Concept description should handle complex data types of the attributes and their aggregations, as necessary.
■ User control versus automation: Online analytical processing in data warehouses is a user-controlled process. The selection of dimensions and the application of OLAP operations (e.g., drill-down, roll-up, slicing, and dicing) are primarily directed and controlled by users. Although the control in most OLAP systems is quite user-friendly, users do require a good understanding of the role of each dimension. Furthermore, in order to find a satisfactory description of the data, users may need to specify a long sequence of OLAP operations. It is often desirable to have a more automated process that helps users determine which dimensions (or attributes) should be included in the analysis, and the degree to which the given data set should be generalized in order to produce an interesting summarization of the data.
This section presents an alternative method for concept description, called attribute-oriented induction, which works for complex data types and relies on a data-driven generalization process.
4.5.1. Attribute-Oriented Induction for Data Characterization
The attribute-oriented induction (AOI) approach to concept description was first proposed in 1989, a few years before the introduction of the data cube approach. The data cube approach is essentially based on materialized views of the data, which typically have been precomputed in a data warehouse. In general, it performs offline aggregation before an OLAP or data mining query is submitted for processing. On the other hand, the attribute-oriented induction approach is basically a query-oriented, generalization-based, online data analysis technique. Note that there is no inherent barrier distinguishing the two approaches based on online aggregation versus offline precomputation. Some aggregations in the data cube can be computed online, while