Results 41 to 50 of about 18,390,026 (306)
An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling
Annals of Data Science, 2022 K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group.
M. Zubair +5 more
semanticscholar +1 more source
Advanced Approach of Material Region Detections on Fibre-Reinforced Concrete CT-Scans
Advances in Electrical and Electronic Engineering, 2017 Detections of material regions on CT-scans of solids are commonly treated manually by an expert. Although such manual detections have many advantages, some amount of human error is also incorporated. Moreover, expert opinions may vary significantly.
Marek Pecha +4 more
doaj +1 more source
JOIV: International Journal on Informatics Visualization, 2023 The menu is one of the most fundamental aspects of business continuity in the culinary industry. One of the tools that can be used for menu analysis is menu engineering.
Nina Setiyawati +2 more
doaj +1 more source
Improved Smoothed Analysis of the k-Means Method [PDF]
, 2008 The k-means method is a widely used clustering algorithm. One of its distinguished features is its speed in practice. Its worst-case running-time, however, is exponential, leaving a gap between practical and theoretical performance.
Manthey, Bodo, Röglin, Heiko
core +6 more sources
Research on K-Value Selection Method of K-Means Clustering Algorithm
ET Journal, 2019 Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, the K-value of clustering needs to be given in advance and the choice of K-value directly affect the ...
Chunhui Yuan, Haitao Yang
semanticscholar +1 more source
On Variants of k-means Clustering [PDF]
, 2015 \textit{Clustering problems} often arise in the fields like data mining, machine learning etc. to group a collection of objects into similar groups with respect to a similarity (or dissimilarity) measure.
Bandyapadhyay, Sayan +1 more
core +2 more sources
PYTHON MÜHİTİNDƏ K-MEANS, K-MEANS++ VƏ MİNİ BATCH K-MEANS ALQORİTMLƏRİNİN MÜQAYİSƏLİ ANALİZİ
Problems of Information Technology, 2021 Məqalədə k-means alqortitmi və onun modifikasiyalarının Python mühitində müxtəlif ölçülü verilənlərə tətbiqi məsələlərinə baxılır. Eyni zamanda ənənəvi k-means klasterləşdirmə alqoritmi və onun modifikasıyalarının mövcud vəziyyəti, imkanları, çatışmazlıqları, meydana çıxan problemlər tədqiq edilmiş və onların həlli üçün təkliflər verilmişdir. k-means++
openaire +1 more source
Privacy-Preserving and Outsourced Multi-User k-Means Clustering [PDF]
, 2014 Many techniques for privacy-preserving data mining (PPDM) have been investigated over the past decade. Often, the entities involved in the data mining process are end-users or organizations with limited computing and storage resources.
Bertino, Elisa +4 more
core +3 more sources
Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce [PDF]
, 2014 The kernel $k$-means is an effective method for data clustering which extends the commonly-used $k$-means algorithm to work on a similarity matrix over complex data structures.
Elgohary, Ahmed +3 more
core +1 more source
, 2018 The recent framework of compressive statistical learning aims at designing tractable learning algorithms that use only a heavily compressed representation-or sketch-of massive datasets.
Jacques, Laurent, Schellekens, Vincent
core +1 more source