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 …

Development of new seed with modified validity measures for k-means clustering

S Manochandar, M Punniyamoorthy… - Computers & Industrial …, 2020 - Elsevier
Conventional k-means clustering is the widely used partitional method, mainly adapted to
machine learning and pattern recognition problems. This algorithm is highly sensitive to …

Condensed silhouette: An optimized filtering process for cluster selection in K-means

A Naghizadeh, DN Metaxas - Procedia Computer Science, 2020 - Elsevier
In K-Means based clustering algorithms, different initial seeds can lead to different clustering
results. Selecting the best result from different initial seeds is called the filtering process. The …

Parkinson's disease severity clustering based on tapping activity on mobile device

D Surangsrirat, P Sri-Iesaranusorn, A Chaiyaroj… - Scientific Reports, 2022 - nature.com
In this study, we investigated the relationship between finger tapping tasks on the
smartphone and the MDS-UPDRS I–II and PDQ-8 using the mPower dataset. mPower is a …

Identifying genetic signatures from single-cell rna sequencing data by matrix imputation and reduced set gene clustering

S Seth, S Mallik, A Islam, T Bhadra, A Roy, PK Singh… - Mathematics, 2023 - mdpi.com
In this current era, the identification of both known and novel cell types, the representation of
cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in …

Clustering algorithms in an educational context: An automatic comparative approach

D Hooshyar, Y Yang, M Pedaste, YM Huang - IEEE Access, 2020 - ieeexplore.ieee.org
Despite an increasing consensus regarding the significance of properly identifying the most
suitable clustering method for a given problem, a surprising amount of educational research …

[PDF][PDF] Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations

I Syukra, A Hidayat, MZ Fauzi - Indonesian Journal of Artificial …, 2019 - researchgate.net
212 Mart Rambutan Street on Pekanbaru City is a company engaged in retail. Meeting the
needs of consumers and making the right decision in determining the sales strategy is a …

LabelForest: Non-parametric semi-supervised learning for activity recognition

Y Ma, H Ghasemzadeh - Proceedings of the AAAI Conference on …, 2019 - ojs.aaai.org
Activity recognition is central to many motion analysis applications ranging from health
assessment to gaming. However, the need for obtaining sufficiently large amounts of labeled …

Partitioning and hierarchical based clustering: a comparative empirical assessment on internal and external indices, accuracy, and time

SI Hassan, A Samad, O Ahmad, A Alam - International Journal of …, 2020 - Springer
Clustering is an unsupervised data mining technique where exploration is done with little
knowledge of data classes. Its aim is to recognize the hidden information from the data for …

A clustering approach for modularizing service-oriented systems

O Ezzat, K Medini, X Boucher, X Delorme - Journal of Intelligent …, 2022 - Springer
Companies are seeking more and more to offer customized goods and services to
customers to be able to satisfy their needs. Several methods emerged to fulfill the needs of …