The KTH-IDOL2 Database

The KTH Visit in Semi-Markov Processes

THE KTH-TIPS2 database

1 Background This document provides a brief Users' Guide to the KTH-TIPS2 image database (KTH is the abbreviation of our university, and TIPS stands for Textures under varying Illumination, Pose and Scale). The KTH-TIPS2 provides a considerable extension to our previous database of images of materials — KTH-TIPS. The guide describes which materials are contained in the database (Section 2), how...

THE KTH-TIPS database

This document provides a brief Users’ Guide to the KTH-TIPS image database (KTH is the abbreviation of our university, and TIPS stands for Textures under varying Illumination, Pose and Scale). The guide describes which materials are contained in the database (Section 2), how images were acquired (Section 3) and subsequently cropped to remove the background (Section 4), and we also discuss some ...

On the Distribution and Moments of Record Values in Increasing Populations

Consider a sequence of n independent observations from a population of increasing size αi, i = 1,2,... and an absolutely continuous initial distribution function. The distribution of the kth record value is represented as a countable mixture, with mixing the distribution of the kth record time and mixed the distribution of the nth order statistic. Precisely, the distribution function and (pow...

An efficient cluster and decomposition algorithm for mining association rules

Conventional algorithms for mining association rules operate in a combination of smaller large itemsets. This paper presents a new efficient which combines both the cluster concept and decomposition of larger candidate itemsets, while proceeds from mining the maximal large itemsets down to large 1-itemsets, named cluster-decomposition association rule (CDAR). First, the CDAR method creates some...