Performance evaluation of clustering algorithms for varying cardinality and dimensionality of data sets
S Renjith, A Sreekumar, M Jathavedan - Materials today: proceedings, 2020 - Elsevier
Clustering is the most widely used unsupervised machine learning technique, having
extensive applications in statistical analysis. We have multiple clustering algorithms …
extensive applications in statistical analysis. We have multiple clustering algorithms …
Sampling-based visual assessment computing techniques for an efficient social data clustering
Visual methods were used for pre-cluster assessment and useful cluster partitions. Existing
visual methods, such as visual assessment tendency (VAT), spectral VAT (SpecVAT), cosine …
visual methods, such as visual assessment tendency (VAT), spectral VAT (SpecVAT), cosine …
Time, space, money, and social interaction: Using machine learning to classify people's mobility strategies through four key dimensions
Previous activity-based studies have shown that behavioural outcomes are the result of
complex and multidimensional processes. In this context, identifying and characterizing …
complex and multidimensional processes. In this context, identifying and characterizing …
Exploring the Role of Multi-Agent Systems in Improving K-Means Clustering Method
Clustering algorithms are attracting much application interest due to the significant growth in
the rate of data generation. However, the high computational complexity of the existing …
the rate of data generation. However, the high computational complexity of the existing …
Detection of pre-cluster nano-tendency through multi-viewpoints cosine-based similarity approach
Pre-clusters assessment is a significant problem in data clustering. It found that visual cluster
tendency assessment (VAT) is majorly focused on addressing the problem of pre-clusters …
tendency assessment (VAT) is majorly focused on addressing the problem of pre-clusters …
An efficient sampling-based visualization technique for big data clustering with crisp partitions
K Rajendra Prasad, M Mohammed… - Distributed and Parallel …, 2021 - Springer
The data cluster tendency is an emerging need for exploring the big data cluster analysis
tasks. The data are evaluated based on the number of clusters is known as cluster tendency …
tasks. The data are evaluated based on the number of clusters is known as cluster tendency …
A Multi-Agent K-Means Algorithm for Improved Parallel Data Clustering
Due to the rapid increase in data volumes, clustering algorithms are now finding
applications in a variety of fields. However, existing clustering techniques have been …
applications in a variety of fields. However, existing clustering techniques have been …
Hybrid visual computing models to discover the clusters assessment of high dimensional big data
M Suleman Basha, SK Mouleeswaran… - Soft Computing, 2023 - Springer
Clusters assessment is a major identified problem in big data clustering. Top big data
partitioning techniques, such as, spherical k-means, Mini-batch-k-means are widely used in …
partitioning techniques, such as, spherical k-means, Mini-batch-k-means are widely used in …
[HTML][HTML] An effective assessment of cluster tendency through sampling based multi-viewpoints visual method
Social networks are the rich sources to people for sharing the knowledge on health-related
issues. Nowadays, Twitter is one of the great significant social platforms to the people for a …
issues. Nowadays, Twitter is one of the great significant social platforms to the people for a …
A comparative analysis of clustering quality based on internal validation indices for dimensionally reduced social media data
S Renjith, A Sreekumar, M Jathavedan - International Conference on …, 2019 - Springer
Almost all modern industries leverage data analytics to deal with various dimensions of their
business like demand forecasting, targeted marketing, and supply chain planning. In …
business like demand forecasting, targeted marketing, and supply chain planning. In …