Data mining has been widely used in different scenarios in society, and clustering analysis is facing new content and challenges, with different clustering algorithms trying to achieve clustering results with differentiated ideas. Therefore, this research will also focus on the basic parts and types ...
In data mining, various methods of clustering algorithms are used to group data objects based on their similarities or dissimilarities. These algorithms can be broadly classified into several types, each with its own characteristics and underlying principles. Let’s explore some of the commonly used ...
Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clu...
Partitioning algorithms are clustering techniques that subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. There are different types of partitioning clustering methods. The most popular is theK-means clustering(MacQueen 1967), in which, ...
Theapplicationofclusteringalgorithmstoscientificres- earchfacesmanychallenges.First,almostallthealgorithms requirepre-specifiedparameters,suchasthenumberofclusters k,asmallpositiverealnumberδthatisusefulwhentestingthe terminalconditions,apositiveintegerseedandsoon.Second, ...
Algorithms for clustering data.Author(s): AK Jain, RC Dubes, R. Dubes Publication date: 1998-01-05 Read this article at ScienceOpen Bookmark There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a ...
Unsupervised clustering of single-cell RNA-sequencing data enables the identification of distinct cell populations. However, the most widely used clustering algorithms are heuristic and do not formally account for statistical uncertainty. We find that not addressing known sources of variability in a stati...
For example, if a huge set of sales data was clustered, information about the data in each cluster might reveal patterns that could be used for targeted marketing.There are several clustering algorithms. One of the most common is called the k-means algorithm. There are several variations of ...
Clustering is performed on the merged similarity matrix by using graph-based clustering algorithms such as spectral22 and Louvain algorithm16. However, similarity matrix-based clustering cannot explicitly consider the dropout events in scRNA-seq data. Hao et al. developed a weighted nearest-neighbor (...
There are many different clustering algorithms. One of the oldest and most widely used is the k-means algorithm. In this article I’ll explain how the k-means algorithm works and present a complete C# demo program. There are many existing standalone data-clustering tools, so why would you ...