We extend the problem of association rule mining-a key data mining problem-to systems in
which the database is partitioned among a very large number of computers that are …

Extraction of frequent patterns in transaction-oriented database is crucial to several data
mining tasks such as association rule generation, time series analysis, classification, etc …

Discovery of sequential patterns is becoming increasingly useful and essential in many
scientific and commercial domains. Enormous sizes of available datasets and possibly large …

Traditional methods for data mining typically make the assumption that the data is
centralized, memory-resident, and static. This assumption is no longer tenable. Such …

Recent advances in computing, communications, and digital storage technologies, together
with development of high throughput data acquisition technologies have made it possible to …

Constraint based mining finds all itemsets that satisfy a set of predicates. Many constraints
can be categorised as being either monotone or antimonotone. Dualminer was the first …

Efficient algorithms for mining frequent itemsets are crucial for mining association rules and
for other data mining tasks. Methods for mining frequent itemsets and for iceberg data cube …

The problem of constructing bounded-error summaries of binary attributed data of very high
dimensions is an important and difficult one. These summaries enable more expensive …

First and foremost, my deepest thanks to my supervisor, Dr. Osmar R. Zaiane for his
invaluable advice and support. His guidance is always insightful, and his patience is highly …

Abstract Knowledge Discovery in Databases (KDD), or Data Mining is used to discover
interesting or useful patterns and relationships in data, with an emphasis on large volume of …