A systematic literature review on identifying patterns using unsupervised clustering algorithms: A data mining perspective
M Chaudhry, I Shafi, M Mahnoor, DLR Vargas… - Symmetry, 2023 - mdpi.com
Data mining is an analytical approach that contributes to achieving a solution to many
problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable …
problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable …
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 …
DENCLUE-IM: A new approach for big data clustering
H Rehioui, A Idrissi, M Abourezq, F Zegrari - Procedia Computer Science, 2016 - Elsevier
Every day, a large volume of data is generated by multiple sources, social networks, mobile
devices, etc. This variety of data sources produce an heterogeneous data, which are …
devices, etc. This variety of data sources produce an heterogeneous data, which are …
New clustering algorithms for twitter sentiment analysis
H Rehioui, A Idrissi - IEEE Systems Journal, 2019 - ieeexplore.ieee.org
In this last decade, the use of social networks became ubiquitous in our daily life. Twitter,
one of the famous social networks became a rich source of discussed topics. The users in …
one of the famous social networks became a rich source of discussed topics. The users in …
A detailed study of clustering algorithms
K Bindra, A Mishra - 2017 6th international conference on …, 2017 - ieeexplore.ieee.org
The foremost illustrative task in data mining process is clustering. It plays an exceedingly
important role in the entire KDD process also as categorizing data is one of the most …
important role in the entire KDD process also as categorizing data is one of the most …
A varied density-based clustering approach for event detection from heterogeneous twitter data
Z Ghaemi, M Farnaghi - ISPRS international journal of geo-information, 2019 - mdpi.com
Extracting the latent knowledge from Twitter by applying spatial clustering on geotagged
tweets provides the ability to discover events and their locations. DBSCAN (density-based …
tweets provides the ability to discover events and their locations. DBSCAN (density-based …
K-means and alternative clustering methods in modern power systems
As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …
and electric vehicles, the complexity of managing these systems increases. With the …
Dynamic spatio-temporal tweet mining for event detection: a case study of hurricane florence
Extracting information about emerging events in large study areas through spatiotemporal
and textual analysis of geotagged tweets provides the possibility of monitoring the current …
and textual analysis of geotagged tweets provides the possibility of monitoring the current …
A fast clustering approach for large multidimensional data
H Rehioui, A Idrissi - International Journal of Business …, 2019 - inderscienceonline.com
Density-based clustering is a strong family of clustering methods. The strength of this family
is its ability to classify data of arbitrary shapes and to omit the noise. Among them density …
is its ability to classify data of arbitrary shapes and to omit the noise. Among them density …
SDE: A novel clustering framework based on sparsity-density entropy
Clustering of data with high dimension and variable densities poses a remarkable challenge
to the traditional density-based clustering methods. Recently, entropy, a numerical measure …
to the traditional density-based clustering methods. Recently, entropy, a numerical measure …