Open Access
Temporal Data Mining: an overview
Cláudia Antunes,Arlindo L. Oliveira +1 more
- 01 Jan 2001
287
TL;DR: A survey on the most significant techniques developed in the past ten years to deal with temporal sequences is provided.
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Abstract: One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. In this case, a complete understanding of the entire phenomenon requires that the data should be viewed as a sequence of events. Temporal sequences appear in a vast range of domains, from engineering, to medicine and finance, and the ability to model and extract information from them is crucial for the advance of the information society. This paper provides a survey on the most significant techniques developed in the past ten years to deal with temporal sequences.
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Citations
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.
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A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects
Ezugwu E. Absalom,Abiodun Motunrayo Ikotun,Olaide Nathaniel Oyelade,Laith Abualigah,Jeffrey O. Agushaka,Christopher Ifeanyi Eke,Andronicus Ayobami Akinyelu +6 more
TL;DR: Clustering is an essential tool in data mining research and applications as discussed by the authors and it is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning.
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Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation
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k-Shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement
Junjing Yang,Chao Ning,Chirag Deb,Fan Zhang,David Cheong,Siew Eang Lee,Chandra Sekhar,Kwok Wai Tham +7 more
TL;DR: This proposed clustering method based on k-shape algorithm is a relatively novel method to identify shape patterns in time-series data and can detect building energy usage patterns in different time granularity effectively and proves that the forecasting accuracy of SVR model is significantly improved by utilizing the results of the proposed clustered method.
184
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Rakesh Agrawal,Ramakrishnan Srikant +1 more
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TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Mining frequent patterns without candidate generation
Jiawei Han,Jian Pei,Yiwen Yin +2 more
- 16 May 2000
TL;DR: This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.
Mining sequential patterns
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 06 Mar 1995
TL;DR: Three algorithms are presented to solve the problem of mining sequential patterns over databases of customer transactions, and empirically evaluating their performance using synthetic data shows that two of them have comparable performance.
•Proceedings Article
Gaussian Processes for Regression
Christopher Williams,Carl Edward Rasmussen +1 more
- 27 Nov 1995
TL;DR: This paper investigates the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations.
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