Matching and indexing sequences of different lengths

Bozkaya, Tolga; Yazdani, Nasser; Ozsoyoglu, Meral

doi:10.1145/266714.266880

Cited by 58 publications

(28 citation statements)

References 4 publications

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“…and designing the distance function that best suits the specifics of the data and the purposes of the analysis. A variety of distance functions have been proposed for trajectories, including the basic Euclidean distance (assuming that trajectories are represented by vectors of fixed length), spatial Euclidean distance average along the time [18], time series-inspired functions such as (dynamic) time warping distance [8] [25] and Least Common Sub-Sequence (LCSS) measure [1] [9], and direction-oriented distances [19] [26].…”

Section: Related Workmentioning

confidence: 99%

Interactive visual clustering of large collections of trajectories

Andrienko

Rinzivillo

Nanni

et al. 2009

2009 IEEE Symposium on Visual Analytics Science and Technology

174

134

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One of the most common operations in exploration and analysis of various kinds of data is clustering, i.e. discovery and interpretation of groups of objects having similar properties and/or behaviors. In clustering, objects are often treated as points in multi-dimensional space of properties. However, structurally complex objects, such as trajectories of moving entities and other kinds of spatiotemporal data, cannot be adequately represented in this manner. Such data require sophisticated and computationally intensive clustering algorithms, which are very hard to scale effectively to large datasets not fitting in the computer main memory. We propose an approach to extracting meaningful clusters from large databases by combining clustering and classification, which are driven by a human analyst through an interactive visual interface.

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Section: Related Workmentioning

confidence: 99%

Interactive visual clustering of large collections of trajectories

Andrienko

Rinzivillo

Nanni

et al. 2009

2009 IEEE Symposium on Visual Analytics Science and Technology

174

134

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show abstract

“…The basic idea is to match two sequences by allowing them to stretch, without rearranging the order of the elements but allowing some elements to be unmatched. Using the LCSS of two sequences, one can define the distance using the length of this subsequence (Agrawal et al, 1995;Bozkaya, Yazdani, & Ozsoyoglu, 1997;Bollobás et al, 1997;.…”

Section: Related Workmentioning

confidence: 99%

Elastic Translation Invariant Matching of Trajectories

Vlachos¹,

Kollios

Gunopulos

2005

Mach Learn

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Abstract.We investigate techniques for analysis and retrieval of object trajectories. We assume that a trajectory is a sequence of two or three dimensional points. Trajectory datasets are very common in environmental applications, mobility experiments, video surveillance and are especially important for the discovery of certain biological patterns. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest Common Subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to the similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translating of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and Dynamic Time Warping distance functions (for real and synthetic data) and show the superiority of our approach, especially under the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach.

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“…The most related papers to our work therefore are Bozkaya [4] and Vlachos [28]. The first discusses similarity measures for sequences of multidimensional points using an approach equivalent to LCSS.…”

Section: Gis'02mentioning

confidence: 99%

Aggregation and comparison of trajectories

Meratnia¹,

By²

2002

Proceedings of the Tenth ACM International Symposium on Advances in Geographic Information Systems - GIS '02

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Dealing with moving objects necessitates having available complete geographical traces for determining exact or possible locations that objects have had, have or will have. This is where trajectory determination plays an important role, and on which classification, aggregation and comparison methods must be built. The purpose of aggregation is to identify similar trajectories and to represent them by a single trajectory.Although much work has been done in similarity measurements for time series data, they mainly deal with one dimensional time series. On the other hand, they are good for short time series and in absence of noise, which is definitely not the case for moving objects. This paper describes different approaches to aggregate similar trajectories.

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Matching and indexing sequences of different lengths

Cited by 58 publications

References 4 publications

Interactive visual clustering of large collections of trajectories

Interactive visual clustering of large collections of trajectories

Elastic Translation Invariant Matching of Trajectories

Aggregation and comparison of trajectories

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