It is something that you can do on your own or with friends or classmates to try to find inspiration in the connection between ideas.
The verb CLUSTER has 2 senses: 1. come together as in a cluster or flock. 2. gather or cause to gather into a cluster. Familiarity information: CLUSTER used as a verb is rare.  Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). 1 Subspace models: in Biclustering (also known as Co-clustering or two-mode-clustering), clusters are modeled with both cluster members and relevant attributes. 2  Group models: some algorithms do not provide a refined model for their results and just provide the grouping information. Clustering Clustering is similar to another process called Brainstorming. Clustering is something that you can do on your own or with friends or classmates to try to find inspiration in the connection between ideas.  k-means clustering is a partitioning method. The function kmeans partitions data into k mutually exclusive clusters, and returns the index of the cluster to which it has assigned each observation. Unlike hierarchical clustering, k-means clustering operates on actual observations (rather than the larger set of dissimilarity measures), and creates a single level of clusters. The distinctions mean that k-means clustering is often more suitable than hierarchical clustering for large amounts of data. Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. 1 Subspace models: in Biclustering (also known as Co-clustering or two-mode-clustering), clusters are modeled with both cluster members and relevant attributes. 2  Group models: some algorithms do not provide a refined model for their results and just provide the grouping information. It was asserted in that the relaxed solution of k-means clustering, specified by the cluster indicators, is given by the PCA (principal component analysis) principal components, and the PCA subspace spanned by the principal directions is identical to the cluster centroid subspace. In cluster analysis, the k-means algorithm can be used to partition the input data set into k partitions (clusters). However, the pure k-means algorithm is not very flexible, and as such is of limited use (except for when vector quantization as above is actually the desired use case!). The goal is to assign a cluster to each data point. K-means is a clustering method that aims to find the positions $\b{\mu}_i,i=1...k$ of the clusters that minimize the distance from the data points to the cluster. Clustering is the process of partitioning a group of data points into a small number of clusters. For instance, the items in a supermarket are clustered in categories (butter, cheese and milk are grouped in dairy products). Of course this is a qualitative kind of partitioning. k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. In cluster analysis, the k-means algorithm can be used to partition the input data set into k partitions (clusters). However, the pure k-means algorithm is not very flexible, and as such is of limited use (except for when vector quantization as above is actually the desired use case!).
http://www.audioenglish.org/dictionary/cluster.htm https://en.wikipedia.org/wiki/Cluster_analysis http://grammar.ccc.commnet.edu/grammar/composition/brainstorm_clustering.htm http://www.mathworks.com/help/stats/k-means-clustering.html https://en.wikipedia.org/wiki/Cluster_analysis https://en.wikipedia.org/wiki/K-means_clustering http://onmyphd.com/?p=k-means.clustering https://en.wikipedia.org/wiki/K-means_clustering
what does clustering mean