1,592 research outputs found
Periodic Pattern Mining a Algorithms and Applications
Owing to a large number of applications periodic pattern mining has been extensively studied for over a decade Periodic pattern is a pattern that repeats itself with a specific period in a give sequence Periodic patterns can be mined from datasets like biological sequences continuous and discrete time series data spatiotemporal data and social networks Periodic patterns are classified based on different criteria Periodic patterns are categorized as frequent periodic patterns and statistically significant patterns based on the frequency of occurrence Frequent periodic patterns are in turn classified as perfect and imperfect periodic patterns full and partial periodic patterns synchronous and asynchronous periodic patterns dense periodic patterns approximate periodic patterns This paper presents a survey of the state of art research on periodic pattern mining algorithms and their application areas A discussion of merits and demerits of these algorithms was given The paper also presents a brief overview of algorithms that can be applied for specific types of datasets like spatiotemporal data and social network
Generation of Two-Voice Imitative Counterpoint from Statistical Models
Generating new music based on rules of counterpoint has been deeply studied in music informatics. In this article, we try to go further, exploring a method for generating new music based on the style of Palestrina, based on combining statistical generation and pattern discovery. A template piece is used for pattern discovery, and the patterns are selected and organized according to a probabilistic distribution, using horizontal viewpoints to describe melodic properties of events. Once the template is covered with patterns, two-voice counterpoint in a florid style is generated into those patterns using a first-order Markov model. The template method solves the problem of coherence and imitation never addressed before in previous research in counterpoint music generation. For constructing the Markov model, vertical slices of pitch and rhythm are compiled over a large corpus of dyads from Palestrina masses. The template enforces different restrictions that filter the possible paths through the generation process. A double backtracking algorithm is implemented to handle cases where no solutions are found at some point within a generation path. Results are evaluated by both information content and listener evaluation, and the paper concludes with a proposed relationship between musical quality and information content. Part of this research has been presented at SMC 2016 in Hamburg, Germany
Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences
The need to analyze information from streams arises in a variety of
applications. One of its fundamental research directions is to mine sequential
patterns over data streams. Current studies mine series of items based on the
presence of the pattern in transactions but pay no attention to the series of
itemsets and their multiple occurrences. The pattern over a window of itemsets
stream and their multiple occurrences, however, provides additional capability
to recognize the essential characteristics of the patterns and the
inter-relationships among them that are unidentifiable by the existing
presence-based studies. In this paper, we study such a new sequential pattern
mining problem and propose a corresponding sequential miner with novel
strategies to prune the search space efficiently. Experiments on both real and
synthetic data show the utility of our approach
Applications of high-frequency telematics for driving behavior analysis
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsProcessing driving data and investigating driving behavior has been receiving an
increasing interest in the last decades, with applications ranging from car insurance
pricing to policy-making. A popular way of analyzing driving behavior is to move
the focus to the maneuvers as they give useful information about the driver who is
performing them.
Previous research on maneuver detection can be divided into two strategies, namely,
1) using fixed thresholds in inertial measurements to define the start and end of specific
maneuvers or 2) using features extracted from rolling windows of sensor data
in a supervised learning model to detect maneuvers. While the first strategy is not
adaptable and requires fine-tuning, the second needs a dataset with labels (which is
time-consuming) and cannot identify maneuvers with different lengths in time.
To tackle these shortcomings, we investigate a new way of identifying maneuvers
from vehicle telematics data, through motif detection in time-series. Using a publicly
available naturalistic driving dataset (the UAH-DriveSet), we conclude that motif
detection algorithms are not only capable of extracting simple maneuvers such as accelerations,
brakes, and turns, but also more complex maneuvers, such as lane changes
and overtaking maneuvers, thus validating motif discovery as a worthwhile line for
future research in driving behavior.
We also propose TripMD, a system that extracts the most relevant driving patterns
from sensor recordings (such as acceleration) and provides a visualization that allows
for an easy investigation. We test TripMD in the same UAH-DriveSet dataset and show
that (1) our system can extract a rich number of driving patterns from a single driver
that are meaningful to understand driving behaviors and (2) our system can be used
to identify the driving behavior of an unknown driver from a set of drivers whose
behavior we know.Nas últimas décadas, o processamento e análise de dados de condução tem recebido
um interesse cada vez maior, com aplicações que abrangem a área de seguros de
automóveis até a atea de regulação. Tipicamente, a análise de condução compreende a
extração e estudo de manobras uma vez que estas contêm informação relevante sobre
a performance do condutor.
A investigação prévia sobre este tema pode ser dividida em dois tipos de estratégias,
a saber, 1) o uso de valores fixos de aceleração para definir o início e fim de cada
manobra ou 2) a utilização de modelos de aprendizagem supervisionada em janelas
temporais. Enquanto o primeiro tipo de estratégias é inflexível e requer afinação dos
parâmetros, o segundo precisa de dados de condução anotados (o que é moroso) e não
é capaz de identificar manobras de diferentes durações.
De forma a mitigar estas lacunas, neste trabalho, aplicamos métodos desenvolvidos
na área de investigação de séries temporais de forma a resolver o problema de deteção
de manobras. Em particular, exploramos área de deteção de motifs em séries temporais
e testamos se estes métodos genéricos são bem-sucedidos na deteção de manobras.
Também propomos o TripMD, um sistema que extrai os padrões de condução mais
relevantes de um conjuntos de viagens e fornece uma simples visualização. TripMD é
testado num conjunto de dados públicos (o UAH-DriveSet), do qual concluímos que
(1) o nosso sistema é capaz de extrair padrões de condução/manobras de um único
condutor que estão relacionados com o perfil de condução do condutor em questão e (2)
o nosso sistema pode ser usado para identificar o perfil de condução de um condutor
desconhecido de um conjunto de condutores cujo comportamento nos é conhecido
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