Now showing items 1-5 of 5
Clustering Quality Improvement of k-means using a Hybrid Evolutionary Model
(ELSEVIER Science BV, 2015)
Choosing good candidates for the initial centroid selection process for compact clustering algorithms, such as k-means, is essential for clustering quality and performance. In this study, a novel hybrid evolutionary model ...
Weather Data Analysis and Sensor Fault Detection Using An Extended IoT Framework with Semantics, Big Data, and Machine Learning
In recent years, big data and Internet of Things (IoT) implementations started getting more attention. Researchers focused on developing big data analytics solutions using machine learning models. Machine learning is a ...
High quality clustering of big data and solving empty-clustering problem with an evolutionary hybrid algorithm
Achieving high quality clustering is one of the most well-known problems in data mining. k-means is by far the most commonly used clustering algorithm. It converges fairly quickly, but achieving a good solution is not ...
Generalized Class Representative Computation with Graph Embedding and Clustering
In this paper, we propose an object category representation framework by first showing objects as graph structures and embedding graphs into vector spaces for color object recognition. The object categories are then built ...
CutESC: Cutting edge spatial clustering technique based on proximity graphs
( Elsevier Ltd, 2019-12)
In this paper, we propose a cut-edge algorithm for spatial clustering (CutESC) based on proximity graphs. The CutESC algorithm removes edges when a cut-edge value for the edge's endpoints is below a threshold. The cut-edge ...