This thesis focuses on classification and clustering techniques using kernel density estimates that can be efficiently implemented using P-trees. Chapter 1 introduces the topics of data mining, classification, clustering, and P-trees. Chapter 2 analyzes bit-column-based data organization and P-trees. Chapter 3 describes P-trees and a new sorting scheme. Chapters 4-7 present various classification and clustering algorithms developed using the P-tree framework, including a kernel-based classifier, a decision tree approach, a semi-naive Bayes classifier, and a hierarchical clustering method. Chapter 8 concludes the thesis.