Trend Analysis Using Agglomerative Hierarchical Clustering Approach for Time Series Big Data

2021 ◽  
pp. 869-876
Author(s):  
P. Subbulakshmi ◽  
S. Vimal ◽  
M. Kaliappan ◽  
Y. Harold Robinson ◽  
Mucheol Kim
Author(s):  
Subbulakshmi Pasupathi ◽  
Vimal Shanmuganathan ◽  
Kaliappan Madasamy ◽  
Harold Robinson Yesudhas ◽  
Mucheol Kim

2020 ◽  
Vol 9 (2) ◽  
pp. 85 ◽  
Author(s):  
David Lamb ◽  
Joni Downs ◽  
Steven Reader

Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail when applied to newer types of data like moving object data and big data. Moving object data incorporates at least three parts: location, time, and attributes. This paper proposes an improved space-time clustering approach that relies on agglomerative hierarchical clustering to identify groupings in movement data. The approach, i.e., space–time hierarchical clustering, incorporates location, time, and attribute information to identify the groups across a nested structure reflective of a hierarchical interpretation of scale. Simulations are used to understand the effects of different parameters, and to compare against existing clustering methodologies. The approach successfully improves on traditional approaches by allowing flexibility to understand both the spatial and temporal components when applied to data. The method is applied to animal tracking data to identify clusters, or hotspots, of activity within the animal’s home range.


Author(s):  
V. Velvizhi ◽  
Satish R. Billewar ◽  
Gaurav Londhe ◽  
Praveen Kshirsagar ◽  
Neeraj Kumar

2019 ◽  
Vol 58 (4) ◽  
pp. 259-270
Author(s):  
Min Soo Kim ◽  
Seung Wook Oh ◽  
Jin-Wook Han

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Majed AlSubih ◽  
Madhuri Kumari ◽  
Javed Mallick ◽  
Raghu Ramakrishnan ◽  
Saiful Islam ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1021
Author(s):  
Zhanserik Nurlan ◽  
Tamara Zhukabayeva ◽  
Mohamed Othman

Wireless sensor networks (WSN) are networks of thousands of nodes installed in a defined physical environment to sense and monitor its state condition. The viability of such a network is directly dependent and limited by the power of batteries supplying the nodes of these networks, which represents a disadvantage of such a network. To improve and extend the life of WSNs, scientists around the world regularly develop various routing protocols that minimize and optimize the energy consumption of sensor network nodes. This article, introduces a new heterogeneous-aware routing protocol well known as Extended Z-SEP Routing Protocol with Hierarchical Clustering Approach for Wireless Heterogeneous Sensor Network or EZ-SEP, where the connection of nodes to a base station (BS) is done via a hybrid method, i.e., a certain amount of nodes communicate with the base station directly, while the remaining ones form a cluster to transfer data. Parameters of the field are unknown, and the field is partitioned into zones depending on the node energy. We reviewed the Z-SEP protocol concerning the election of the cluster head (CH) and its communication with BS and presented a novel extended mechanism for the selection of the CH based on remaining residual energy. In addition, EZ-SEP is weighted up using various estimation schemes such as base station repositioning, altering the field density, and variable nodes energy for comparison with the previous parent algorithm. EZ-SEP was executed and compared to routing protocols such as Z-SEP, SEP, and LEACH. The proposed algorithm performed using the MATLAB R2016b simulator. Simulation results show that our proposed extended version performs better than Z-SEP in the stability period due to an increase in the number of active nodes by 48%, in efficiency of network by the high packet delivery coefficient by 16% and optimizes the average power consumption compared to by 34.


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