Unsupervised Pattern Discovery for Multimedia Sequences Lexing Xie This thesis investigates the problem of discovering patterns from multimedia sequences. The problem is of interest as capturing and storing large amounts of multimedia data has become commonplace, yet our capability to process, interpret, and use these rich corpora has notably lagged behind. Patterns refer to the the recurrent a...

Large-Scale Pattern Discovery in Music Thierry Bertin-Mahieux This work focuses on extracting patterns in musical data from very large collections. The problem is split in two parts. First, we build such a large collection, the Million Song Dataset, to provide researchers access to commercial-size datasets. Second, we use this collection to study cover song recognition which involves finding ha...

We present a data mining system, EasyMiner which has been developed for interactive mining of interesting patterns in time-oriented databases. This system implements a wide spectrum of data mining functions, including generalisation, characterisation, classification, association and relevant analysis. By enhancing several interesting data mining techniques, including attribute induction and ass...

Human listeners are able to recognize structure in music through the perception of repetition and other relationships within a piece of music. This work aims to automate the task of music analysis. Music is “explained” in terms of embedded relationships, especially repetition of segments or phrases. The steps in this process are the transcription of audio into a representation with a similarity...

We present a method for modeling user navigation on a web site using grammatical inference of stochastic regular grammars. With this method we achieve better models than the previously used first order Markov chains, in terms of predictive accuracy and utility of recommendations. In order to obtain comparable results, we apply the same grammatical inference algorithms on Markov chains, modeled ...

This note reports on a novel method of detection of haplotype blocks in the human genome. It is based upon pattern discovery using technology being patented by Cira Discovery Sciences, Inc. It is intended as a demonstration of our pattern discovery technology in the context of genomic variation (SNP) data. Actual applications in genomic analyses will take advantage of technological advances tha...

Algorithm FDMSP (fast distributed mining of sequential patterns) is proposed in order to deal with mining sequential patterns in distributed environment and its properties are analyzed. The algorithm utilizes prefix-projected technique to divide the pattern searching space, utilizes polling site associated with prefix to get a global support, and utilizes local pruning, poll pruning and count p...

Electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximize their marketing effort. The discovery of marketing related navigation patterns requires the development of data mining algorithms capable of discovering sequential access patterns from web logs. This paper introduces a new algorithm called MiDAS that ...

In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries). Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally find correlations and hidden variables, which summarise the key trends in the entire stream collection. It can do this quickly, with no buffering of stream values and without comparing pairs of strea...

Microarray technology provides an opportunity to monitor mRNA levels of expression of thousands of genes simultaneously in a single experiment. The enormous amount of data produced by this high throughput approach presents a challenge for data analysis: to extract meaningful patterns, to evaluate its quality and to interpret the results. The most commonly used method of identifying such pattern...