BIRCH: an efficient data clustering method for very large databases
T Zhang, R Ramakrishnan, M Livny - ACM sigmod record, 1996 - dl.acm.org
Finding useful patterns in large datasets has attracted considerable interest recently, and
one of the most widely studied problems in this area is the identification of clusters, or …
one of the most widely studied problems in this area is the identification of clusters, or …
An incremental CFS algorithm for clustering large data in industrial internet of things
With the rapid advances of sensing technologies and wireless communications, large
amounts of dynamic data pertaining to industrial production are being collected from many …
amounts of dynamic data pertaining to industrial production are being collected from many …
Comparative study of single linkage, complete linkage, and ward method of agglomerative clustering
S Sharma, N Batra - … on machine learning, big data, cloud …, 2019 - ieeexplore.ieee.org
Clustering is the process of grouping the datasets into various clusters in such a way which
leads to maximum inter-cluster dissimilarity but maximum intra-cluster similarity. Clustering …
leads to maximum inter-cluster dissimilarity but maximum intra-cluster similarity. Clustering …
[图书][B] Unsupervised classification: similarity measures, classical and metaheuristic approaches, and applications
S Bandyopadhyay, S Saha - 2013 - Springer
Clustering is an important unsupervised classification technique where data points are
grouped such that points that are similar in some sense belong to the same cluster. Cluster …
grouped such that points that are similar in some sense belong to the same cluster. Cluster …
A novel approach for generating routable road maps from vehicle GPS traces
J Wang, X Rui, X Song, X Tan, C Wang… - International Journal of …, 2015 - Taylor & Francis
Public vehicles and personal navigation assistants have become increasingly equipped with
single-frequency global positioning system (GPS) receivers or loggers. These commonly …
single-frequency global positioning system (GPS) receivers or loggers. These commonly …
A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospects
Clustering is a set of essential mathematical techniques in artificial intelligence and machine
learning for analyzing massive amounts of data generated by applications. Clustering uses …
learning for analyzing massive amounts of data generated by applications. Clustering uses …
[PDF][PDF] A brief survey of unsupervised agglomerative hierarchical clustering schemes
S Sreedhar Kumar, M Madheswaran… - Int J Eng Technol …, 2019 - researchgate.net
Unsupervised hierarchical clustering process is a mathematical model or exploratory tool
aims to provide the easiest way to categorize the distinct groups over the large volume of …
aims to provide the easiest way to categorize the distinct groups over the large volume of …
ICFS clustering with multiple representatives for large data
With the prevailing development of Cyber-physical-social systems and Internet of Things,
large-scale data have been collected consistently. Mining large data effectively and …
large-scale data have been collected consistently. Mining large data effectively and …
Comparative analysis of clustering algorithms with heart disease datasets using data mining Weka tool
The heart is the important organ on the human (men or women) body. Life is totally
dependent over efficient working of the heart. What if a heart undergoes a disorder …
dependent over efficient working of the heart. What if a heart undergoes a disorder …
Penalty parameter selection for hierarchical data stream clustering
A Bhagat, N Kshirsagar, P Khodke, K Dongre… - Procedia Computer …, 2016 - Elsevier
Extracting useful information from large sets of data is the main task of data mining.
Clustering is one of the most commonly used data mining technique. Data streams are …
Clustering is one of the most commonly used data mining technique. Data streams are …