Clustering and aggregating clues of trajectories for mining trajectory patterns and routes

Hung, Chih‐Chieh; Peng, Wen-Chih; Lee, Wang-Chien

doi:10.1007/s00778-011-0262-6

Cited by 162 publications

(93 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 1 gives an overview of the NoiseClust algorithm including some steps as follows: (1) given a description of the taxi GPS data, our approach first used four taxi GPS data sets to explain the OD of the GPS data based trajectory (see Section 2.1); (2) real taxi GPS data sequences are used to encode chromosomes (see Section 2.2); (3) an initial population approach is proposed using noise, and K-means++ to initialize seeds (see Section 2.3); (4) DBI (Davis-Boudin index) [32] is employed as a fitness function; (5) in the genetic operation, a gene rearrangement technique based on cosine, and adaptive probabilities of crossover and mutation, are used for genetic operation (see Section 2.5); (6) a density estimation method is presented to divide the number of niches, and a share function also is used for the genetic operation, which are considered as the niche genetic algorithm (NGA) (see Section 2.6); (7) in Section 2.7, an elitism strategy is used to select the best chromosome, which is used to replace the worst chromosome in the current iteration; (8) the best chromosome is obtained to use K-means clustering; (9) the termination condition of K-means clustering is given in Section 2.9; and (10) the complexity of the NoiseClust algorithm is analyzed in Section 2.1. algorithm (NGA) (see Section 2.6); (7) in Section 2.7, an elitism strategy is used to select the best chromosome, which is used to replace the worst chromosome in the current iteration; (8) the best chromosome is obtained to use K-means clustering; (9) the termination condition of K-means clustering is given in Section 2.9; and (10) the complexity of the NoiseClust algorithm is analyzed in Section 2.1. …”

Section: The Noiseclust Clustering Algorithmmentioning

confidence: 99%

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou

Shen

et al. 2017

IJGI

View full text Add to dashboard Cite

Rapidly growing Global Positioning System (GPS) data plays an important role in trajectory and their applications (e.g., GPS-enabled smart devices). In order to employ K-means to mine the better origins and destinations (OD) behind the GPS data and overcome its shortcomings including slowness of convergence, sensitivity to initial seeds selection, and getting stuck in a local optimum, this paper proposes and focuses on a novel niche genetic algorithm (NGA) with density and noise for K-means clustering (NoiseClust). In NoiseClust, an improved noise method and K-means++ are proposed to produce the initial population and capture higher quality seeds that can automatically determine the proper number of clusters, and also handle the different sizes and shapes of genes. A density-based method is presented to divide the number of niches, with its aim to maintain population diversity. Adaptive probabilities of crossover and mutation are also employed to prevent the convergence to a local optimum. Finally, the centers (the best chromosome) are obtained and then fed into the K-means as initial seeds to generate even higher quality clustering results by allowing the initial seeds to readjust as needed. Experimental results based on taxi GPS data sets demonstrate that NoiseClust has high performance and effectiveness, and easily mine the city's situations in four taxi GPS data sets.

show abstract

Section: The Noiseclust Clustering Algorithmmentioning

confidence: 99%

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou

Shen

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

“…Several researchers have recognized that partitional cluster algorithms area unit like-minded for cluster large document data sets thanks to their relatively low method wants (Steinbach, Karypis & Kumar, 2000). The presence of logical structure clues inside the document, scientific criteria and math similarity measures are primarily accustomed figure thematically coherent, contiguous text blocks in unstructured documents (Lee, Han & Whang, 2007;Hung, Peng & Lee, 2015;MacQueen, 1967;Ferrari, & De Castro, 2015). Use PLSA to cipher word-topic distributions, fold in those distributions at the block level, and so choose segmentation points supported the similarity values of adjacent block pairs.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The presence of logical structure clues within the document, scientific criteria and applied math similarity measures chiefly accustomed figure thematically coherent, contiguous text blocks in unstructured documents (Sun et al, 2008;Zhang et al, 2014;Rangel et al, 2016). Recent segmentation techniques have taken advantage of advances in generative topic modeling algorithms, which were specifically designed to spot issues at intervals text to cipher word-topic distributions (Lee, Han &Whang, 2007;Hung, Peng& Lee, 2015).…”

Section: Background and Related Workmentioning

confidence: 99%

Design and Develop Semantic Textual Document Clustering Model

Fahad¹

2017

JCSIT

View full text Add to dashboard Cite

The utilization of textual documents is spontaneously increasing over the internet, email, web pages, reports, journals, articles and they stored in the electronic database format. It is challenging to find and access these documents without proper classification mechanisms. To overcome such difficulties we proposed a semantic document clustering model and develop this model. The document pre-processing steps, semantic information from WordNet help us to be bioavailable the semantic relation from raw text. By reminding the limitation of traditional clustering algorithms on the natural language, we consider semantic clustering by COBWEB conceptual clustering. Clustering quality and high accuracy were one of the most important aims of our research, and we chose F-Measure evaluation for ensuring the purity of clustering. However, there still exist many challenges, like the word, high spatial property, extracting core linguistics from texts, and assignment adequate description for the generated clusters. By the help of Word Net database, we eliminate those issues. In this research paper, there have a proposed framework and describe our development evaluation with evaluation.

show abstract

“…Trajectories have also been clustered using similarity measures [8,9]. Some popular similarity measures which have been used to compare trajectories are Euclidian distance [10], LCSS (Least Common Subsequence) [11], DTW (Dynamic Time Warping) [12], ERP (Edit distance with Real Penalty) [13], EDR (Edit Distance on Real sequences) [9], and CATS (Clue Aware Trajectory Similarity) [14]. Without getting help from a complementary method such as sliding window, these similarity measures can only find the similarity between complete trajectories and will not give any information about the common sub-trajectories.…”

Section: Introductionmentioning

confidence: 99%

Finding frequently visited paths: Dealing with the uncertainty of spatio-temporal mobility data

Baratchi

Meratnia

Havinga

2013

2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing

View full text Add to dashboard Cite

Abstract-With the ever-increasing advancements in sensor technology and localization systems, large amounts of spatiotemporal data can be collected from moving objects equipped with wireless sensor nodes. Analysis of such data provides the opportunity of extracting useful information about movement behaviour and interaction between moving objects. Inherent characteristics of wireless sensor nodes cause the data collected by them to have low or irregular frequency and often be erroneous. Existence of different levels of uncertainty in these data makes the procedure of finding movement patterns difficult and ambiguous. In this paper, we propose a hierarchical approach to find the frequently visited paths using location data of people carrying a custom designed mobile wireless sensor node. We hierarchically cluster trajectories and find their resemblance at the finest level while dealing with the uncertainties. The performance evaluation results show that compared with previous schemes, our method performs better in presence of ambiguity and sources of data uncertainty.

show abstract

Clustering and aggregating clues of trajectories for mining trajectory patterns and routes

Cited by 162 publications

References 29 publications

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Design and Develop Semantic Textual Document Clustering Model

Finding frequently visited paths: Dealing with the uncertainty of spatio-temporal mobility data

Contact Info

Product

Resources

About