Experimental Study II

This chapter provides an experimental study of the proposed model on Adult data set. The chapter includes the implementation of pattern extraction from this dataset by following a series of steps as discussed before. It also includes detailed implementation of pattern prediction of numeric variables, nominal variables, and aggregate data. The implementation of pattern prediction is also a series of steps as discussed before.

Conceptual Model for Predictive Analysis on Large Data

This chapter provides an overview of the proposed model for pattern extraction and pattern prediction over data warehouses. As discussed before, the main objective of the research is to provide a single model for pattern extraction and prediction. The objectives include an automated way to select variables for the mining process, automated schema design, advanced evaluation of extracted patterns, and visualization of extracted patterns.

Experimental Study III

This chapter provides implementation of the proposed model on Forest Cover Type data set. The chapter includes the implementation of pattern extraction from this dataset by following a series of steps discussed in the proposed model chapter. It also includes detailed implementation of pattern prediction from Automobile dataset for prediction of numeric variables, nominal variables, and aggregate data. The implementation of pattern prediction is also a series of steps as discussed before.

Experimental Study I

This chapter provides implementation of the proposed model on Automobile data set. The chapter includes the implementation of pattern extraction from this dataset by following a series of steps discussed in the proposed model chapter. It also includes detailed implementation of pattern prediction from Automobile dataset for prediction of numeric variables, nominal variables, and aggregate data. The implementation of pattern prediction is also a series of steps as discussed before.

Literature Review

The purpose of this literature review chapter is to discuss the integrated techniques of knowledge discovery, identify gaps, and draw research objectives of this research. The chapter firstly discusses the pattern extraction techniques from large datasets, for example, a data warehouse, followed by pattern prediction techniques. A review of pattern extraction and prediction is presented on the basis of knowledge independency, multi-level mining ability, advanced evaluation of results, and visualization ability. At the end, a summary of issues in the current research are presented followed by the research objectives of this research.

Introduction

This chapter introduces the context in general, the aims, and the rationale of the book. It follows this with a brief review of the contents of chapters in this book. The motivation for the multi-level pattern extraction and prediction is given, followed by identifying different limitations in existing techniques in this subject to satisfy the requirements of multi-level pattern extraction and prediction.