An Adaptive Trajectory Clustering Method Based on Grid and Density in Mobile Pattern Analysis

Mao, Yingchi; Zhong, Haishi; Qi, Hai; Ping, Ping; Li, Xiaofang

doi:10.3390/s17092013

Cited by 41 publications

(31 citation statements)

References 20 publications

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“…Variable n refers to the number of topics whose term probability is not equal to zero. For the defuzzification, the final output of fuzzy z is obtained by calculating the mean value, as in (7). The value of z is used as the probability value of term p for topic k and will be used for the next sampling process until it reaches convergent conditions.…”

Section: Term Weigthing With Fuzzy Luhn's Gibbs Ldamentioning

confidence: 99%

“…The techniques in clustering include partitioning, hierarchical, grid-based, and density-based method [2]. These methods have been used in various applications such as K-Means for SME Risk Analysis Documents [3], KPrototype for clustering big data based on MapReduce [4], K-Means for earthquake cluster analysis [5], Ward's linkage method for classifying the languages [6], grid and density-based for trajectory clustering [7], and DBSCAN for categorizing districts [8]. The hierarchical clustering method uses a tree concept, which is divided into agglomerative and divisive approaches [9].…”

Section: Introductionmentioning

confidence: 99%

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Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

Prihatini¹,

Putra²,

Giriantari³

et al. 2019

IJECE

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Agglomerative hierarchical is a bottom up clustering method, where the distances between documents can be retrieved by extracting feature values using a topic-based latent dirichlet allocation method. To reduce the number of features, term selection can be done using Luhn's Idea. Those methods can be used to build the better clusters for document. But, there is less research discusses it. Therefore, in this research, the term weighting calculation uses Luhn's Idea to select the terms by defining upper and lower cut-off, and then extracts the feature of terms using gibbs sampling latent dirichlet allocation combined with term frequency and fuzzy Sugeno method.The feature values used to be the distance between documents, and clustered with single, complete and average link algorithm. The evaluations show the feature extraction with and without lower cut-off have less difference. But, the topic determination of each term based on term frequency and fuzzy Sugeno method is better than Tsukamoto method in finding more relevant documents. The used of lower cut-off and fuzzy Sugeno gibbs latent dirichlet allocation for complete agglomerative hierarchical clustering have consistent metric values. This clustering method suggested as a better method in clustering documents that is more relevant to its gold standard.

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Section: Term Weigthing With Fuzzy Luhn's Gibbs Ldamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

Prihatini¹,

Putra²,

Giriantari³

et al. 2019

IJECE

View full text Add to dashboard Cite

show abstract

“…It is noteworthy that there are a few recent works focused on clustering motion trajectories using different methods including distance-based clustering [35], waypoint clustering [36], treebased methods [37], grid-based methods [38], and kernel methods [39]. Some of these methods focus specifically on airspace monitoring [36].…”

Section: Related Workmentioning

confidence: 99%

A unified framework for joint mobility prediction and object profiling of drones in UAV networks

et al. 2018

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In recent years, using a network of autonomous and cooperative unmanned aerial vehicles (UAVs) without command and communication from the ground station has become more imperative, in particular in search-and-rescue operations, disaster management, and other applications where human intervention is limited. In such scenarios, UAVs can make more efficient decisions if they acquire more information about the mobility, sensing and actuation capabilities of their neighbor nodes. In this paper, we develop an unsupervised online learning algorithm for joint mobility prediction and object profiling of UAVs to facilitate control and communication protocols. The proposed method not only predicts the future locations of the surrounding flying objects, but also classifies them into different groups with similar levels of maneuverability (e.g. rotatory, and fixed-wing UAVs) without prior knowledge about these classes. This method is flexible in admitting new object types with unknown mobility profiles, thereby applicable to emerging flying Ad-hoc networks with heterogeneous nodes.

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“…As such, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was applied to solve this problem. DBSCAN identifies random and non-convex shape clusters and determining the number of clusters in advance is not required [28,29]. We used Liu's [30] …”

Section: Rc Area Identificationmentioning

confidence: 99%

Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories

Yang

Wang

2018

IJGI

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Urban congestion can be classified into two types: Recurrent Congestion (RC) and Non-Recurrent Congestion (NRC). RC is more regular than NRC, having fixed and long-standing patterns. Mining urban recurrent congestion evolution patterns can assist with congestion cause analysis and the creation of alleviating strategies. Most existing methods for analyzing urban congestion patterns are based on traditional traffic detector data, which is inflexible and expensive. Additionally, prior research primarily focused on the microscopic model, which simulated congestion propagation based on theoretical models and hypothetical networks. As such, most previous models and methods are difficult to apply to real case scenarios. Therefore, we investigated recurrent congestion patterns by mining historical taxi trajectory data that were collected in Harbin, China. A three-step method is proposed to reveal urban recurrent congestion evolution patterns. Firstly, a grid-based congestion detection method is presented by calculating the change in taxi global positioning system (GPS) trajectory patterns. Secondly, a customized cluster algorithm is applied to measure the recurrent congestion area. Finally, a series of indicators are proposed to reflect RC evolution patterns. A case study was competed in the Harbin urban area to evaluate the main methods. Finally, RC cause analysis and alleviating strategy are discussed. The results study are expected to provide a better understanding of urban RC evolution patterns.

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An Adaptive Trajectory Clustering Method Based on Grid and Density in Mobile Pattern Analysis

Cited by 41 publications

References 20 publications

Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

A unified framework for joint mobility prediction and object profiling of drones in UAV networks

Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories

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