[PDF][PDF] Research article standardization and its effects on k-means clustering algorithm
IB Mohamad, D Usman - Res J Appl Sci Eng Technol, 2013 - pdfs.semanticscholar.org
Data clustering is an important data exploration technique with many applications in data
mining. K-means is one of the most well known methods of data mining that partitions a …
mining. K-means is one of the most well known methods of data mining that partitions a …
Customer segmentation using K-means clustering
T Kansal, S Bahuguna, V Singh… - 2018 international …, 2018 - ieeexplore.ieee.org
The zeitgeist of modern era is innovation, where everyone is embroiled into competition to
be better than others. Today's business run on the basis of such innovation having ability to …
be better than others. Today's business run on the basis of such innovation having ability to …
HML-IDS: A hybrid-multilevel anomaly prediction approach for intrusion detection in SCADA systems
Critical infrastructures, eg, electricity generation and dispersal networks, chemical
processing plants, and gas distribution, are governed and monitored by supervisory control …
processing plants, and gas distribution, are governed and monitored by supervisory control …
How did the COVID-19 pandemic impact traveler behavior toward public transport? The case of Athens, Greece
The COVID-19 outbreak led to significant changes in daily commuting. As lockdowns were
imposed to metropolitan areas throughout the globe, travelers refrained heavily from using …
imposed to metropolitan areas throughout the globe, travelers refrained heavily from using …
Feature selection based on brain storm optimization for data classification
Brain storm optimization (BSO) is a new and effective swarm intelligence method inspired by
the human brainstorming process. This paper presents a novel BSO-based feature selection …
the human brainstorming process. This paper presents a novel BSO-based feature selection …
[PDF][PDF] Investigations on impact of feature normalization techniques on classifier's performance in breast tumor classification
BK Singh, K Verma, AS Thoke - International Journal of Computer …, 2015 - Citeseer
Feature extraction and feature normalization is an important preprocessing technique,
usually employed before classification. Feature normalization is a useful step to restrict the …
usually employed before classification. Feature normalization is a useful step to restrict the …
A simple, fully automated shoreline detection algorithm for high-resolution multi-spectral imagery
This paper develops and validates a new fully automated procedure for shoreline
delineation from high-resolution multispectral satellite images. The model is based on a new …
delineation from high-resolution multispectral satellite images. The model is based on a new …
The best clustering algorithms in data mining
In data mining, Clustering is the most popular, powerful and commonly used unsupervised
learning technique. It is a way of locating similar data objects into clusters based on some …
learning technique. It is a way of locating similar data objects into clusters based on some …
Decarbonization pathways of worldwide energy systems–Definition and modeling of archetypes
M Kueppers, SNP Pineda, M Metzger, M Huber… - Applied Energy, 2021 - Elsevier
Energy system models help to find the optimal technology mixes for decarbonization
strategies in countries worldwide. To reduce the modeling effort and analyze as many …
strategies in countries worldwide. To reduce the modeling effort and analyze as many …
A hybrid model of fuzzy min–max and brain storm optimization for feature selection and data classification
Swarm intelligence (SI)-based optimization methods have been extensively used to tackle
feature selection problems. A feature selection method extracts the most significant features …
feature selection problems. A feature selection method extracts the most significant features …