A comparative analysis of trajectory similarity measures

2021 ◽  
pp. 1-27
Author(s):  
Yaguang Tao ◽  
Alan Both ◽  
Rodrigo I. Silveira ◽  
Kevin Buchin ◽  
Stef Sijben ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Similarity Measures ◽  
Trajectory Similarity

scholarly journals Evaluating the effect of compressing algorithms for trajectory similarity and classification problems

2021 ◽  
Author(s):  
Antonios Makris ◽  
Camila Leite da Silva ◽  
Vania Bogorny ◽  
Luis Otavio Alvares ◽  
Jose Antonio Macedo ◽  
...  
Keyword(s):  
Trajectory Analysis ◽  
Similarity Measures ◽  
Classification Problems ◽  
Trajectory Data ◽  
Compression Algorithms ◽  
Time Ratio ◽  
Ratio Speed ◽  
Trajectory Similarity ◽  
Real World Datasets ◽  
The Impact

AbstractDuring the last few years the volumes of the data that synthesize trajectories have expanded to unparalleled quantities. This growth is challenging traditional trajectory analysis approaches and solutions are sought in other domains. In this work, we focus on data compression techniques with the intention to minimize the size of trajectory data, while, at the same time, minimizing the impact on the trajectory analysis methods. To this extent, we evaluate five lossy compression algorithms: Douglas-Peucker (DP), Time Ratio (TR), Speed Based (SP), Time Ratio Speed Based (TR_SP) and Speed Based Time Ratio (SP_TR). The comparison is performed using four distinct real world datasets against six different dynamically assigned thresholds. The effectiveness of the compression is evaluated using classification techniques and similarity measures. The results showed that there is a trade-off between the compression rate and the achieved quality. The is no “best algorithm” for every case and the choice of the proper compression algorithm is an application-dependent process.

scholarly journals A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Jin Zhu ◽  
Dayu Cheng ◽  
Weiwei Zhang ◽  
Ci Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Semantic Information ◽  
Edit Distance ◽  
Similarity Measures ◽  
Indoor Positioning ◽  
Synthetic Dataset ◽  
Shopping Mall ◽  
Indoor Space ◽  
Trajectory Similarity ◽  
Indoor Spaces

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

scholarly journals An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Wang ◽  
Xinzheng Niu ◽  
Jiahui Zhu ◽  
Zuoyan Liu
Keyword(s):  
Community Detection ◽  
Trajectory Analysis ◽  
Moving Objects ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Detection Algorithm ◽  
Similarity Matrix ◽  
Trajectory Data ◽  
Trajectory Similarity ◽  
Community Detection Algorithm

Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

scholarly journals Graph-based Representation for Sentence Similarity Measure : A Comparative Analysis

2018 ◽  
Vol 7 (2.14) ◽  
pp. 32
Author(s):  
Siti Sakira Kamaruddin ◽  
Yuhanis Yusof ◽  
Nur Azzah Abu Bakar ◽  
Mohamed Ahmed Tayie ◽  
Ghaith Abdulsattar A.Jabbar Alkubaisi
Keyword(s):  
Comparative Analysis ◽  
Similarity Measure ◽  
Semantic Analysis ◽  
Similarity Measures ◽  
Jaccard Similarity ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Sentence Level ◽  
Textual Data ◽  
Document Level

Textual data are a rich source of knowledge; hence, sentence comparison has become one of the important tasks in text mining related works. Most previous work in text comparison are performed at document level, research suggest that comparing sentence level text is a non-trivial problem.  One of the reason is two sentences can convey the same meaning with totally dissimilar words.  This paper presents the results of a comparative analysis on three representation schemes i.e. term frequency inverse document frequency, Latent Semantic Analysis and Graph based representation using three similarity measures i.e. Cosine, Dice coefficient and Jaccard similarity to compare the similarity of sentences.  Results reveal that the graph based representation and the Jaccard similarity measure outperforms the others in terms of precision, recall and F-measures. 

Comparing Apple to Amazon: Just a Matter of Words in Machine Learning World

2021 ◽  
Author(s):  
MV Shivaani
Keyword(s):  
Comparative Analysis ◽  
Financial Analysis ◽  
Similarity Measures ◽  
Annual Reports ◽  
Use Case ◽  
Jaccard Similarity ◽  
R Software ◽  
Competitor Analysis ◽  
Unstructured Information ◽  
A Company

Comparative analysis commands special attention in financial analysis as it not only facilitates understanding of  year-on-year changes but also of trends in the performance and position of a company. It is often a go-to tool for competitor analysis. In this note, I illustrate the use of  R (software), its allied packages, and textual analysis algorithms to extend the use of comparative analysis to ‘unstructured’ information presented in the MD&A section of annual reports. For this use case, I consider two giant tech rivals, Apple and Amazon, and present a comparative analysis of their MD&A section using Cosine and Jaccard similarity measures. I also compare the most important words based on tf-idf and sentiments for each company and across the two companies. When supplemented with financial information, comparative analysis can offer novel insights for analysts, managers, researchers, and academics and is a valuable tool to include in accounting curricula.

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