Multidimensional Analysis And Descriptive Mining Of Complex Data Objects

Introduction:

Generalization of Class Composition Hierarchies: An attribute of an object may be composed of or described by another object, some of whose attributes may be in turn composed of or described by other objects, thus forming a class composition hierarchy. Generalization on a class composition hierarchy can be viewed as generalization on a set of nested structured data (which are possibly infinite, if the nesting is recursive).

In principle, the reference to a composite object may traverse via a long sequence of references along the corresponding class composition hierarchy. However, in most cases, the longer the sequence of references traversed, the weaker the semantic linkage between the original object and the referenced composite object. For example, an attribute vehicles owned of an object class student could refer to another object class car, which may contain an attribute auto dealer, which may refer to attributes describing the dealer’s manager and children. Obviously, it is unlikely that any interesting general regularities exist between a student and her car dealer’s manager’s children. Therefore, generalization on a class of objects should be performed on the descriptive attribute values and methods of the class, with limited reference to its closely related components via its closely related linkages in the class composition hierarchy. That is, in order to discover interesting knowledge, generalization should be performed on the objects in the class composition hierarchy that are closely related in semantics to the currently focused class(es), but not on those that have only remote and rather weak semantic linkages.

Construction and Mining of Object Cubes

In an object database, data generalization and multi dimensional analysis are not applied to individual objects but to classes of objects. Since a set of objects in a class may share many attributes and methods, and the generalization of each attribute and method may apply a sequence of generalization operators, the major issue becomes how to make the generalization processes cooperate among different attributes and methods in the class (es).

“So, how can class-based generalization are performed for a large set of objects?” For class based generalization, the attribute-oriented induction method developed in Chapter 4 for mining characteristics of relational databases can be extended to mine data characteristics in object databases. Consider that a generalization-based data mining process can be viewed as the application of a sequence of class-based generalization operators on different attributes. Generalization can continue until the resulting class contains a small number of generalized objects that can be summarized as a concise, generalized rule in high-level terms. For efficient implementation, the generalization of multidimensional attributes of a complex object class can be performed by examining each attribute (or dimension), generalizing each attribute to simple-valued data, and constructing a multidimensional data cube, called an object cube. Once an object cube is constructed, multidimensional analysis and data mining can be performed on it in a manner similar to that for relational data cubes.

Notice that from the application point of view, it is not always desirable to generalize a set of values to single-valued data. Consider the attribute keyword, which may contain a set of keywords describing a book. It does not make much sense to generalize this set of keywords to one single value. In this context, it is difficult to construct an object cube containing the keyword dimension. We will address some progress in this direction in the next section when discussing spatial data cube construction. However, it remains a challenging research issue to develop techniques for handling set-valued data effectively in object cube construction and object-based multidimensional analysis.

Generalization-Based Mining of Plan Databases by Divide-and-Conquer: To show how generalization can play an important role in mining complex databases, we examine a case of mining significant patterns of successful actions in a plan database using a divide-and-conquer strategy. A plan consists of a variable sequence of actions. A plan database, or simply a plan base, is a large collection of plans. Plan mining is the task of mining significant patterns or knowledge from a planbase. Plan mining can be used to discover travel patterns of business passengers in an air flight database or to find significant patterns from the sequences of actions in the repair of automobiles. Plan mining is different from sequential pattern mining, where a large number of frequently occurring sequences are mined at a very detailed level. Instead, plan mining is the extraction of important or significant generalized (sequential) patterns from a planbase.