Journal ArticleDOI
A global averaging method for dynamic time warping, with applications to clustering
TLDR
A global technique for averaging a set of sequences is developed, which avoids using iterative pairwise averaging and is thus insensitive to ordering effects, and a new strategy to reduce the length of the resulting average sequence is described.About:
This article is published in Pattern Recognition.The article was published on 2011-03-01. It has received 823 citations till now. The article focuses on the topics: Dynamic time warping & Cluster analysis.read more
Citations
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Journal ArticleDOI
Time-series clustering - A decade review
TL;DR: This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time- series approaches during the last decade and enlighten new paths for future works.
Journal ArticleDOI
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods
TL;DR: In this paper, the authors focus on the challenging problem of hyperspectral image classification, which has recently gained in popularity and attracted the interest of other scientific disciplines such as machine learning, image processing, and computer vision.
Proceedings ArticleDOI
k-Shape: Efficient and Accurate Clustering of Time Series
John Paparrizos,Luis Gravano +1 more
TL;DR: K-Shape as discussed by the authors uses a normalized version of the cross-correlation measure in order to consider the shapes of time series while comparing them, and develops a method to compute cluster centroids, which are used in every iteration to update the assignment of the time series to clusters.
Journal ArticleDOI
Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances
TL;DR: A critical review of the recent advances in DA approaches for remote sensing is provided and an overview of DA methods divided into four categories: 1) invariant feature selection, 2) representation matching, 3) adaptation of classifiers, and 4) selective sampling are presented.
Journal ArticleDOI
Minimum redundancy maximum relevance feature selection approach for temporal gene expression data
Milos Radovic,Mohamed Ghalwash,Mohamed Ghalwash,Mohamed Ghalwash,Nenad Filipovic,Zoran Obradovic +5 more
TL;DR: A filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria is developed, which outperforms alternatives widely used in gene expression studies.
References
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Journal ArticleDOI
Data clustering: a review
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Journal ArticleDOI
A general method applicable to the search for similarities in the amino acid sequence of two proteins
TL;DR: A computer adaptable method for finding similarities in the amino acid sequences of two proteins has been developed and it is possible to determine whether significant homology exists between the proteins to trace their possible evolutionary development.
Journal ArticleDOI
MUSCLE: a multiple sequence alignment method with reduced time and space complexity
TL;DR: MUSCLE offers a range of options that provide improved speed and / or alignment accuracy compared with currently available programs, and a new option, MUSCLE-fast, designed for high-throughput applications.
Journal ArticleDOI
T-Coffee: A novel method for fast and accurate multiple sequence alignment.
TL;DR: A new method for multiple sequence alignment that provides a dramatic improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives but avoids the most serious pitfalls caused by the greedy nature of this algorithm.
Journal ArticleDOI
Dynamic programming algorithm optimization for spoken word recognition
TL;DR: This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition, in which the warping function slope is restricted so as to improve discrimination between words in different categories.