Empirical Analysis of Data Clustering Algorithms

Nerurkar, Pranav; Shirke, Archana; Chandane, Madhav; Bhirud, S. G.

doi:10.1016/j.procs.2017.12.099

Cited by 74 publications

(47 citation statements)

References 16 publications

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“…Clustering is a machine clustering is the Logic used to in Machine learning programming to train the system based on previous data, learning method applied to group (cluster) the principle component that have similar characteristics/ data points. The outcome is that data points within the same group are more similar between them than other data points in other cluster [12]. There are two approaches are available like Hierarchical, Partition and further subdivided into different types [11,12].…”

Section: Methodsmentioning

confidence: 99%

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Envision of Route Safety Direction Using Machine Learning

Pavate¹,

Chaudhari²,

Bansode³

2019

Act Scie Medic

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Section: Methodsmentioning

confidence: 99%

“…The outcome is that data points within the same group are more similar between them than other data points in other cluster [12]. There are two approaches are available like Hierarchical, Partition and further subdivided into different types [11,12]. Table 3 represents the sample of dataset.…”

Section: Methodsmentioning

confidence: 99%

Envision of Route Safety Direction Using Machine Learning

Pavate¹,

Chaudhari²,

Bansode³

2019

Act Scie Medic

View full text Add to dashboard Cite

“…In the case of a flight data (log) containing real data captured from embedded sensors, such as the one used in this work, there is a significant amount available for extracting information. This is a task that is not trivial [62]. Consequently, techniques that use methods that combine knowledge areas such as machine learning, pattern recognition, and information retrieval can be employed towards a better analysis of the data [62].…”

Section: Fcm and Anfismentioning

confidence: 99%

“…This is a task that is not trivial [62]. Consequently, techniques that use methods that combine knowledge areas such as machine learning, pattern recognition, and information retrieval can be employed towards a better analysis of the data [62].…”

Section: Fcm and Anfismentioning

confidence: 99%

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Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

Paulino

Guimarães

Shiguemori

2019

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Nowadays, there is a remarkable world trend in employing UAVs and drones for diverse applications. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. Nevertheless, they depend on positioning systems that may be vulnerable. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming to improve navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. However, its computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a low-cost Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence (HACI) -the cascaded use of Fuzzy C-Means Clustering (FCM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithms -that have been shown able to improve considerably the accuracy of current positioning estimation systems for real-time UAV autonomous navigation, reducing the error in approximately 300cm 2 . In addition, the proposed methodology outperformed two other Computational Intelligence methodologies -Artificial Neural Networks and Regression Models, with 18 different tested approaches -in estimating an UAV position considering the Root-Mean-Square Error against the real trajectory. The generated Fuzzy Inference System has proved to be effective in providing an improved positioning estimation with a low computational burden, about 600 times faster than the fastest embedded sensor refresh rate.

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Minimum spanning tree‐based cluster analysis: A new algorithm for determining inconsistent edges

Şaar

Topcu

2021

Concurrency and Computation

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In recent years, graph‐based data clustering algorithms have become popular as they perform connectivity‐based rather than centroid‐based partitioning. Methods related to minimum spanning tree (MST)‐based data clustering are types of graph‐based algorithms that can recognize arbitrary shapes of clusters by eliminating inconsistent edges from MST graphs. In all MST‐based data clustering algorithms, definition of inconsistent edges is the main problem that needs to be addressed. The longest edges in MST graphs are considered as inconsistent edges under ideal conditions. Nevertheless, outliers often exist in real‐world tasks, which makes the longest edges inaccurate cluster separation indicators. In this paper, we propose a new data clustering algorithm using MST and a critical distance method. The proposed algorithm solves the main issue of MST‐based data clustering, namely identifying and removing inconsistent edges to obtain clusters even in the event that the dataset contains some outliers. It begins by constructing the MST over a given weighted graph based on Euclidean distance and then splits up the graph into clusters by eliminating inconsistent edges using critical distance as a threshold. Integration of the advantages of both MST and critical distance methodology to obtain optimal clusters is the main contribution of this work. The conducted experimental analysis and results using different datasets prove that our proposed clustering algorithm yields better overall performance compared with the most common data clustering algorithms. Taking the Liver and Tumor datasets as an example, the proposed algorithm outperforms all other clustering algorithms with clustering accuracy equal to 0.579 and 0.660, respectively.

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Empirical Analysis of Data Clustering Algorithms

Cited by 74 publications

References 16 publications

Envision of Route Safety Direction Using Machine Learning

Envision of Route Safety Direction Using Machine Learning

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

Minimum spanning tree‐based cluster analysis: A new algorithm for determining inconsistent edges

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