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[Page no. 19] "by using trajectory extraction of moving objects in video, with which, a surveillance system can ﬁnd crowd in public area and predict accidents as well as provide alarms to the monitoring personnel. Boukhers et al. (2015) introduced a method to extract 3D trajectories of objects from 2D videos. In their work, the problems of the inconsistency between object detection and depth estimation results are overcome by particle ﬁlter. Their work signiﬁcantly expands the information of extracted trajecto- ries in video data and has a wide range of applications in movement analysis from video data. 7 Conclusion and future work Clusteringisanefﬁcientwaytoanalyzeandﬁndthemassive,hidden,unknownandinteresting knowledge in large scale dataset, which facilitates the rapid development of data mining technology in recent decades. With the development of location based service, moving object clustering becomes a burgeoning topic in related ﬁelds as an essential part of data mining technology. In this paper, the research status and new development of moving object clustering algorithms in recent years have been surveyed and summarized. Firstly, the representative clustering algorithms proposed in recent years are analyzed and summarized from algorith- mic thinking, key technology and the advantages and disadvantages. Then, popular similarity measures are discussed. Thirdly, some typical valid criterions of cluster result are summa- rized. Lastly, some application scenarios are pointed out and discussed. Onthebasisofsummarizingandsurveyingonthemovingobjectclusteringanditstheories, methods, techniques. We also summarize the problems and the challenges existed in moving object data mining, which mainly includes the following aspects: (1) Most of the current 123  A review of moving object trajectory clustering algorithms trajectory clustering algorithms cannot fully combine time and space dimensions, and they just regard time as the additional dimension of trajectory object. (2) When clustering results is converted to knowledge, some problems lead to a considerable part of them being either too complex to be intuitively understandable or"

[Page no. 18] "the cluster and objects outside the cluster. Based on the analysis above, we can conclude that Cohesion and Separation are much more suitable for validating clustering algorithms which are hierarchy-based and partition-based. 6 Application scenarios of clustering results 1. Moving object life pattern analysis is a typical application scenario of trajectory cluster- ing, which can be detected by aggregating the trajectories of moving objects that have similar motion patterns. 2. Moving object activity prediction is a very important application scenario of moving object trajectory clustering. According to different clustered properties, for example, we can obtain the most possibility of a path that a moving object may move on. However, without temporal information, we just can predict the possible path, but can’t predict the possible time. Therefore, if temporal information can be introduced to moving object clustering, the prediction results will become more precise. 3. Through analyzing the trajectories of moving objects in urban area or with the constraint of road network can identify moving objects’ activities, which are very helpful for smart city plan and smart transportation management. In our previous work Yuan et al. (2013), we proposed a clustering-based approach to ﬁnd areas where mobile users stay for long timeandwithfrequentvisits.Combingwithsemanticconcept,allkindsofusers’activities can be derived from the found areas, which are the data support to city plan and smart transportation management. 4. Outliers (anomalies) detection is the by-product of trajectory clustering. In the processes of clustering, some trajectories (or segments) which are signiﬁcantly different from tra- jectories (or segments) in terms of some similarity metric may be viewed as outliers. The outliers may be an unusual route or an unexpected moving pattern, and they are not the noise but an important part of moving object life style. 123  G. Yuan et al. 5. Movement pattern analysis from video is another"

[Page no. 13] "clustering algorithm, which embodies CenTra at its update procedure. Chen et al. (2012) propose a sketch-based clustering algorithm for uncertain trajectories. In his algorithm, a candidate segments set is constructed to represent uncertain trajectories model precisely based on the M-level Hilbert curve spatial partitioning. The uncertainty is very useful for privacy preserving in many areas, Zhou et al. (2014) was the ﬁrst person to present the idea of transforming the trajectory to an uncertain area for clustering to solve the drawback of (k, d) anonymity model. He proposed a new method called Restore Its True to protect the privacy of trajectory data in publish- ing. Wang et al. (2015) used the uncertainty of trajectory data and tried to ﬁnd uncertain group patterns. In order to measure the distance of two uncertain locations, he put forward an 123  A review of moving object trajectory clustering algorithms Expected Distance Function to compute the similarity at each timestamp. Then the objects were clustered according to their spatial proximity. The application of uncertain trajectory clustering includes two aspects. One is to ﬁnd move pattern in trajectories with noise or errors cause by device and environment, and another is to discovery knowledge in trajectories which are under protected. Therefore, trajectory uncertainty is a two-edged sword. On one hand, it can affect the precision of data analysis algorithms. In many clustering algorithms, we work hard to reduce the uncertainty of a trajectory. Therefore, in this case, the most difﬁcult task is the building of an uncertainty model. On the other hand, uncertainty may protect a moving object’s privacy that could be leaked from its trajectories. Therefore, in this case, we need to make a trajectory more uncertain. 4.5 Semantic trajectory clustering The raw trajectory data itself is a sequence consisting of sampled points including moving"

[Page no. 10] "with different trajectory segments length. It also can measure more the similarity between trajectory segments comprehensively. In our previous work (Yuan et al. 2012), comprehensive trajectory structures are extracted and a structure similarity measures is proposed to compare trajectories (segments) in micro-level. 4 Trajectory clustering algorithms In this section, we give the survey on the algorithms of clustering moving object with their trajectories. Currently, trajectory clustering becomes an attractive topic in moving object data mining ﬁeld. Current trajectory clustering researches mainly focus on three aspects. The ﬁrst is trying to extract full trajectory features (including raw spatiotemporal information, speed, direction, acceleration and other features) and ﬁnd movement patterns in a comprehensive way. The second is trying to ﬁnd suitable distance measurements which can ﬁnd the diver- gence between trajectories effectively and reliably. The third is trying to develop efﬁcient algorithms, which are scalable and ﬂexible in both running time and space. Moreover, many 123  G. Yuan et al. advanced spatial data structures are used to store trajectories, making the trajectory search cost signiﬁcantly reduced. As mentioned above, trajectory data is different from the static data used in traditional algorithms. Due to its serialization, triviality and redundancy, many traditional clustering algorithms can’t be directly used for trajectory data. Therefore, many trajectory clustering studies focus on extending the well known traditional clustering algorithms and apply them in trajectory data, e.g., K-means, BIRCH, DBSCAN, OPTICS, and STING. Compared with traditional clustering techniques, the extension for trajectory clustering requires an efﬁcient and reliable distance measurement to ﬁnd the divergence between trajectories along the time dimension, and a scalable and ﬂexible improvement. On basis of full analysis on moving object clustering, the algorithms can be divided into 5 categories which are listed as follows. 4.1 Spatial based clustering Spatial information is one of the"

[Page no. 4] "a plurality of levels of the rectangular units is present. These units form a hierarchical structure: each high-level unit is divided into a plurality of lower-level units, statistical information about the attributes of each grid unit is previously calculated and stored, and these statistical parameters are useful for query processing. Model based methods have been widely used and have been shown promising results in many applications involving complex data. This kind of algorithms assume a model for each cluster, and tie to ﬁnd the best ﬁtting data for the given model. A model clustering algorithm locates clusters by building density functions which reﬂect the spatial distribution of the data points. It tries to optimize the adaptability between given data and some mathematical models. The model clustering algorithms mainly include the statistical method and the neural network method. COBWEB (Fisher 1987) is the typical algorithm, and it is an incremental system for hierarchical conceptual clustering. The input data of COBWEB is described as classiﬁcation of attribute-value pairs and organized into a classiﬁcation tree incrementally. The algorithm does not require user para- meters. However, there are also many limitations: The algorithm assumed that the probability distribution on each attribute is independent. However, due to the interrelated attributes, the assumption is not always true. In addition, the expression by the probability distribution of clustering needs a quite expensive cost to update and store clustering clusters because the complexity of time and space is not only dependent on the number of properties, but also on the number of values of each property. This cost increases when the quantity of properties is very large. Model based clustering algorithms often face the difﬁculty that they should ﬁnd a proper model for computing data points and their relations. Therefore, many researches, such as Zhong and Ghosh (2003),"

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