Spatial Big Data and Moving Objects: A Comprehensive Survey <s> E. OBJECT-BASED LEARNING <s> Clustering in spatial data mining is to group similar objects based on their distance, connectivity, or their relative density in space. In the real world there exist many physical obstacles such as rivers, lakes and highways, and their presence may affect the result of clustering substantially. We study the problem of clustering in the presence of obstacles and define it as a COD (Clustering with Obstructed Distance) problem. As a solution to this problem, we propose a scalable clustering algorithm, called COD-CLARANS. We discuss various forms of pre-processed information that could enhance the efficiency of COD-CLARANS. In the strictest sense, the COD problem can be treated as a change in distance function and thus could be handled by current clustering algorithms by changing the distance function. However, we show that by pushing the task of handling obstacles into COD-CLARANS instead of abstracting it at the distance function level, more optimization can be done in the form of a pruning function E'. We conduct various performance studies to show that COD-CLARANS is both efficient and effective. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> E. OBJECT-BASED LEARNING <s> A scintillation scanner having an automatic limit switch system for setting the limits of travel of the radiation detection device which is carried by a scanning boom. The automatic limit switch system incorporates position responsive circuitry for developing a signal representative of the position of the boom, reference signal circuitry for developing a signal representative of a selected limit of travel of the boom, and comparator circuitry for comparing these signals in order to control the operation of a boom drive and indexing mechanism. <s> BIB002
The object movement in V2V, sensor networks, or D2D networks has its own unique characteristic which is extremely important for learning driven models and algorithms. The spatial temporal relationship in traffic flow data and geographical information are important considerations for efficient learning-driven systems. It is important to distinguish between two close but unrelated data points that may wrongly be clustered in the same group  , BIB002 . Similarly, the persistent congestion in V2V networks might arise due to many reasons including the network congestion, accidents, and so forth BIB001 . The same scenarios are true for sensor networks as well as the other networks involving moving objects. Therefore, it is extremely important to incorporate diverse patterns in the learning systems which are ultimately applicable to real-life scenarios.