Big data clustering: a review
Clustering is an essential data mining and tool for analyzing big data. There are difficulties
for applying clustering techniques to big data duo to new challenges that are raised with big …
for applying clustering techniques to big data duo to new challenges that are raised with big …
[BOOK][B] Data clustering: theory, algorithms, and applications
The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …
2007. Starting with the common ground and knowledge for data clustering, the monograph …
Dual: Acceleration of clustering algorithms using digital-based processing in-memory
Today's applications generate a large amount of data that need to be processed by learning
algorithms. In practice, the majority of the data are not associated with any labels …
algorithms. In practice, the majority of the data are not associated with any labels …
A survey on parallel clustering algorithms for big data
Data clustering is one of the most studied data mining tasks. It aims, through various
methods, to discover previously unknown groups within the data sets. In the past years …
methods, to discover previously unknown groups within the data sets. In the past years …
A review of clustering algorithms for big data
K Djouzi, K Beghdad-Bey - 2019 International Conference on …, 2019 - ieeexplore.ieee.org
Big data is usually defined by five (05) characteristics called 5Vs+ 1C (Volume, Velocity,
Variety, Veracity, Value and Complexity). It means to data that are too large, dynamic and …
Variety, Veracity, Value and Complexity). It means to data that are too large, dynamic and …
Enhancement over DBSCAN satellite spatial data clustering
MS Al-Batah, ER Al-Kwaldeh… - Journal of Electrical …, 2024 - Wiley Online Library
Image processing is a promising technique for enhancing images or extracting useful
information from them. One commonly used density‐based clustering algorithm is DBSCAN …
information from them. One commonly used density‐based clustering algorithm is DBSCAN …
[PDF][PDF] Choosing DBSCAN parameters automatically using differential evolution
Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with
Noise) has been widely applied in many areas of science due to its simplicity, robustness …
Noise) has been widely applied in many areas of science due to its simplicity, robustness …
[BOOK][B] Cluster analysis and applications
For several years, parts of the content of this textbook have been used in undergraduate
courses in the Department of Mathematics and in the Faculty of Economics at the University …
courses in the Department of Mathematics and in the Faculty of Economics at the University …
An efficient and scalable density-based clustering algorithm for datasets with complex structures
As a research branch of data mining, clustering, as an unsupervised learning scheme,
focuses on assigning objects in the dataset into several groups, called clusters, without any …
focuses on assigning objects in the dataset into several groups, called clusters, without any …
DBSCAN++: Towards fast and scalable density clustering
J Jang, H Jiang - International conference on machine …, 2019 - proceedings.mlr.press
DBSCAN is a classical density-based clustering procedure with tremendous practical
relevance. However, DBSCAN implicitly needs to compute the empirical density for each …
relevance. However, DBSCAN implicitly needs to compute the empirical density for each …