Results 121 to 130 of about 940,524 (345)

Feature Selection in k-Median Clustering

, 2004
An e ective method for selecting features in clustering unlabeled data is proposed based on changing the objective function of the standard k-median clustering algorithm.
Mangasarian, Olvi, Wild, Edward
core  

Techniques for clustering gene expression data [PDF]

, 2008
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate
Kerr, Gráinne, Crane, Martin, M. Crane, H. J. Ruskin, Doolan, Padraig, Ruskin, Heather J., G. Kerr, P. Doolan   +7 more
core   +1 more source

Clustering techniques and innovation-based comparison in Londrina and Region companies

Semina: Ciências Exatas e Tecnológicas
Innovation is often considered a cornerstone for success across various companies. However, research focused on measuring and describing innovation frequently relies on classical statistical techniques.
Ana Paula Barbosa de Morais, Matheus Santos Dias, Bruno Samways dos Santos, Rafael Henrique Palma Lima, Pedro Rochavetz de Lara Andrade   +4 more
doaj   +1 more source

k-means clustering for persistent homology

Advances in Data Analysis and Classification
Abstract Persistent homology is a methodology central to topological data analysis that extracts and summarizes the topological features within a dataset as a persistence diagram. It has recently gained much popularity from its myriad successful applications to many domains, however, its algebraic construction induces a metric space of ...
Yueqi Cao, Prudence Leung, Anthea Monod
openaire   +2 more sources

Circular RNA expression landscapes in myelodysplastic neoplasms: Associations with mutational signatures and disease progression

Molecular Oncology, EarlyView.
In this explorative study, the abundance of circular RNA molecules in bone marrow stem cells was found to be elevated in patients with high‐risk myelodysplastic neoplasms, and to be associated with an increased risk of progression to acute myeloid leukemia.
Eileen Wedge, Morten Tulstrup, Gabriele Todisco, Pedro Moura, Christophe Côme, Jakob Werner Hansen, Jakob Schmidt Jespersen, Balthasar Schlotmann, Maria Creignou, Mette Dahl, Claudia Schöllkopf, Klas Raaschou‐Jensen, Krister Wennerberg, Bo Porse, Joachim Weischenfeldt, Eva Hellström‐Lindberg, Lasse Sommer Kristensen, Kirsten Grønbæk   +17 more
wiley   +1 more source

A novel centroids initialisation for K-means clustering in the presence of benign outliers [PDF]

K-means is one of the most important and widely applied clustering algorithms in learning systems. However, it suffers from centroids initialisation that makes K-means algorithm unstable.
Ghazanfar, Mustansar Ali, Karami, Amin, Ur Rehman, Shafiq   +2 more
core   +1 more source

Profiling Academic Library Patrons using K-means and X-means Clustering

International Journal of Technology, 2019
Information technology is now used very often, especially by individuals born between 1982 and 2002 (the Millennial generation). The academic library, which from its beginnings has been a storehouse for information through collections, is becoming ...
Aisyah Larasati, Apif Miftahul Hajji, Anik Nur Handayani, Nabila Azzahra, Muhammad Farhan, Puji Rahmawati   +5 more
doaj   +1 more source

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Algoritma Modified K-Means Clustering pada Penentuan Cluster Centre Berbasis Sum Of Squared Error (Sse)

, 2014
One of techniques popular inData Mining is clustering. Defenition clustering in scientific from data miningis some of data or objectsin one group or clusters into cluster so each cluster will containthedataas closely aspossibleanddifferent objects in ...
Nainggolan, Rena
core  

Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang

Semesta Teknika, 2016
The selection process among outstanding students in a department has a big problem. This process is not fair because only involve one criteria and ignore the other criteria.
Asroni Asroni, Ronald Adrian
doaj