A novel, adaptive hierarchical clustering approach for independent component sorting for block-wise ICA filtering

2021 ◽  
Author(s):  
Abbie Weeks ◽  
Jaime Tierney Stanton ◽  
Brett C. Byram
Keyword(s):  
Hierarchical Clustering ◽  
Independent Component ◽  
Clustering Approach

scholarly journals EZ-SEP: Extended Z-SEP Routing Protocol with Hierarchical Clustering Approach for Wireless Heterogeneous Sensor Network

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1021
Author(s):  
Zhanserik Nurlan ◽  
Tamara Zhukabayeva ◽  
Mohamed Othman
Keyword(s):  
Sensor Network ◽  
Hierarchical Clustering ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Cluster Head ◽  
Average Power ◽  
Base Station ◽  
Average Power Consumption ◽  
Clustering Approach ◽  
Heterogeneous Sensor Network

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.

scholarly journals Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2972 ◽  
Author(s):  
Jorge Rodríguez ◽  
Ivana Semanjski ◽  
Sidharta Gautama ◽  
Nico Van de Weghe ◽  
Daniel Ochoa
Keyword(s):  
Hierarchical Clustering ◽  
Market Segmentation ◽  
Wearable Sensors ◽  
Essential Element ◽  
Cost Effective ◽  
Mobile Phone Data ◽  
Mobility Data ◽  
Tourism Management ◽  
Clustering Approach ◽  
Spatio Temporal

Understanding tourism related behavior and traveling patterns is an essential element of transportation system planning and tourism management at tourism destinations. Traditionally, tourism market segmentation is conducted to recognize tourist’s profiles for which personalized services can be provided. Today, the availability of wearable sensors, such as smartphones, holds the potential to tackle data collection problems of paper-based surveys and deliver relevant mobility data in a timely and cost-effective way. In this paper, we develop and implement a hierarchical clustering approach for smartphone geo-localized data to detect meaningful tourism related market segments. For these segments, we provide detailed insights into their characteristics and related mobility behavior. The applicability of the proposed approach is demonstrated on a use case in the Province of Zeeland in the Netherlands. We collected data from 1505 users during five months using the Zeeland app. The proposed approach resulted in two major clusters and four sub-clusters which we were able to interpret based on their spatio-temporal patterns and the recurrence of their visiting patterns to the region.

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
Keyword(s):  
Time Series ◽  
Big Data ◽  
Trend Analysis ◽  
Hierarchical Clustering ◽  
Agglomerative Hierarchical Clustering ◽  
Clustering Approach

scholarly journals Space-Time Hierarchical Clustering for Identifying Clusters in Spatiotemporal Point Data

2020 ◽  
Vol 9 (2) ◽  
pp. 85 ◽  
Author(s):  
David Lamb ◽  
Joni Downs ◽  
Steven Reader
Keyword(s):  
Hierarchical Clustering ◽  
A Priori ◽  
Space Time ◽  
Moving Object ◽  
Agglomerative Hierarchical Clustering ◽  
Movement Data ◽  
Clustering Approach ◽  
Nested Structure ◽  
Point Data ◽  
Traditional Approaches

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.

scholarly journals A Novel Hierarchical Clustering Approach Based on Universal Gravitation

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Peng Zhang ◽  
Kun She
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Gravitational Force ◽  
Clustering Algorithms ◽  
Influence Coefficient ◽  
Data Sets ◽  
Universal Gravitation ◽  
Real World Data ◽  
Gravitational Influence ◽  
Clustering Approach

The target of the clustering analysis is to group a set of data points into several clusters based on the similarity or distance. The similarity or distance is usually a scalar used in numerous traditional clustering algorithms. Nevertheless, a vector, such as data gravitational force, contains more information than a scalar and can be applied in clustering analysis to promote clustering performance. Therefore, this paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation. In the first stage, a sparse gravitational graph is constructed based on the top k data gravitations between each data point and its neighbors in the local region. Then the sparse graph is partitioned into many subgraphs by the gravitational influence coefficient. In the last stage, the satisfactory clustering result is obtained by merging these subgraphs iteratively by using a new linkage criterion. To demonstrate the performance of GHC algorithm, the experiments on synthetic and real-world data sets are conducted, and the results show that the GHC algorithm achieves better performance than the other existing clustering algorithms.

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