New approaches to normalization techniques to enhance k-means clustering algorithm

New approaches to normalization techniques to enhance k-means clustering algorithm

Clustering is fundamentally one of the leading origin of basic data mining tools, which makes researchers believe the normal grouping of attributes in datasets. The main aim of clustering is to ascertain similarities and arrangements with a large dataset by partitioning data into clusters. It is imp...

Full description

Bibliographic Details
Main Authors:	Dalatu, Paul Inuwa, Midi, Habshah
Format:	Article
Language:	English
Published:	Institute for Mathematical Research, Universiti Putra Malaysia 2020
Online Access:	http://psasir.upm.edu.my/id/eprint/38339/1/3.%20Paul%20n%20Habshah.pdf

Similar Items

Improved normalization and standardization techniques for higher purity in K-means clustering
by: Dalatu, Paul Inuwa, et al.
Published: (2016)

Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
by: Dalatu, Paul Inuwa
Published: (2018)

Statistical estimators as an alternative to standard deviation in weighted Euclidean distance cluster analysis
by: Dalatu, Paul Inuwa, et al.
Published: (2018)

The performance of k-means clustering method based on robust principal components
by: Kadom, Ahmed, et al.
Published: (2018)

Cluster optimization in VANET using MFO algorithm and K-Means clustering
by: Ramlee, Sham Rizal, et al.
Published: (2023)

Clustering of rainfall data using k-means algorithm
by: Mohd Sham, Mohamad, et al.
Published: (2019)

Clustering Student Performance Data Using k-Means Algorithms
by: Sultan Alalawi, Sultan Juma, et al.
Published: (2023)

Gene selection for high dimensional data using k-means clustering algorithm and statistical approach
by: Ahmad, Farzana Kabir, et al.
Published: (2014)

Integrated bisect K-means and firefly algorithm for hierarchical text clustering
by: Mohammed, Athraa Jasim, et al.
Published: (2016)

Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering
by: Rahmah, Mokhtar, et al.
Published: (2021)

Clustering techniques for human posture recognition: K-Means, FCM and SOM
by: Kiran, Maleeha, et al.
Published: (2009)

Cyclic exchange neighborhood search technique for the K-means clustering problem
by: Thirathon, Nattavude, 1980-
Published: (2005)

Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
by: Wan Maseri, Wan Mohd, et al.

Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique
by: Mohammed Abdo Al-Qershi, Osamah
Published: (2018)

Improving K-Means Clustering Algorithm Using Discretization Technique in Network Intrusion Detection System (S/O: 13141)
by: Mohamad Tahir, Hatim, et al.
Published: (2020)

Clustering for binary data sets by using genetic algorithm-incremental K-means
by: Saharan, S., et al.
Published: (2018)

A Hybrid K-Means Hierarchical Algorithm for Natural Disaster Mitigation Clustering
by: Prasetyadi, Abdurrakhman, et al.
Published: (2022)

Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
by: Sulaiman, Md. Nasir, et al.
Published: (2018)

Asymptotic properties of K-means clustering algorithm as a density estimation procedure
by: Wong, M. Anthony.
Published: (2009)

MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
by: Wan Maseri, Wan Mohd, et al.
Published: (2012)

Identification of natural disaster impacted electricity load profiles with k means clustering algorithm
by: Jessen, Simon Hedegard, et al.
Published: (2022)

Using genetic algorithms in K-means fast learning artificial neural networks for clustering
by: Yin, Xiang
Published: (2008)

Approximate k-means clustering through random projections
by: Persu, Elena-Mădălina
Published: (2015)

Modeling of vehicle trajectory using K-means and fuzzy C-means clustering
by: Choong, Mei Yeen, et al.
Published: (2019)

A global k-means approach for autonomous cluster initialization of probabilistic neural network
by: Chang, R.K.Y., et al.
Published: (2008)

Intrusion detection systems using K-means clustering system
by: Nor Dzuhairah Hani, Jamaludin
Published: (2019)

Dictionary training for sparse representation as generalization of K-means clustering
by: Sahoo, Sujit Kumar, et al.
Published: (2013)

Extracting feature from images by using K-Means clustering algorithm / Abdul Hakim Zainal Abidin
by: Zainal Abidin, Abdul Hakim
Published: (2016)

Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach
by: Nurnajmin Qasrina Ann, ., et al.
Published: (2022)

Pattern discovery using k-means algorithm
by: Ahmed, Almahdi Mohammed, et al.
Published: (2014)

Intrusion detection based on K-means clustering and Naïve Bayes classification
by: Muda, Zaiton, et al.
Published: (2011)

Intrusion detection based on k-means clustering and OneR classification
by: Muda, Zaiton, et al.
Published: (2011)

K-means clustering to improve the accuracy of decision tree response classification.
by: Ali, S. A., et al.
Published: (2009)

An optimization framework of K-means clustering and metaheuristic for traveling salesman problem
by: Wang, Benquan
Published: (2021)

Influential measures on log-normal model for left-truncated and case-k interval censored data with time-dependent covariate
by: Manoharan, Thirunanthini, et al.
Published: (2020)

Toward misreading : assembling (k)new meaning
by: Dennis, Nolan Oswald
Published: (2018)

Extensions to the K-AMH algorithm for numerical clustering
by: Seman, Ali, et al.
Published: (2018)

Business sustainability practice (BSP) profile assessment in Malaysia economic sectors using K-mean clustering approach
by: Nur Soliha, Sahimi
Published: (2020)

Efficient tag grouping RFID anti-collision algorithm for internet of things applications based on improved k-means clustering
by: Umelo, Nnamdi Henry, et al.
Published: (2023)

A performance comparison of euclidean, manhattan and minkowski distances in k-means clustering
by: Haviluddin Haviluddin, et al.
Published: (2021)