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The Access Method Services utility program IDCAMS is commonly used to manipulate ("delete and define") VSAM data sets. Custom programs can access VSAM datasets through Data Definition (DD) statements in Job Control Language (JCL), via dynamic allocation or in online regions such as in Customer Information Control System (CICS).
A key-sequenced data set (KSDS) is a type of data set used by IBM's VSAM computer data storage system. [ 1 ] : 5 Each record in a KSDS data file is embedded with a unique key. [ 1 ] : 20 A KSDS consists of two parts, the data component and a separate index file known as the index component which allows the system to physically locate the record ...
An example of cluster sampling is area sampling or geographical cluster sampling.Each cluster is a geographical area in an area sampling frame.Because a geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by grouping several respondents within a local area into a cluster.
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 ...
Although cluster sampling and stratified sampling bear some superficial similarities, they are substantially different. In stratified sampling, a random sample is drawn from all the strata, where in cluster sampling only the selected clusters are studied, either in single- or multi-stage. Advantages. Cost and speed that the survey can be done in
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms.Also called cluster ensembles [1] or aggregation of clustering (or partitions), it refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better ...
In computer science, data stream clustering is defined as the clustering of data that arrive continuously such as telephone records, multimedia data, financial transactions etc. Data stream clustering is usually studied as a streaming algorithm and the objective is, given a sequence of points, to construct a good clustering of the stream, using a small amount of memory and time.