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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.
The Multiple Indicator Cluster Surveys (MICS) are household surveys implemented by countries under the programme developed by the United Nations Children's Fund to provide internationally comparable, statistically rigorous data on the situation of children and women.
This is a significant advance over MFT's memory management, but has some weaknesses: if a job allocates memory dynamically (as most sort programs and database management systems do), the programmers has to estimate the job's maximum memory requirement and pre-define it for MVT. A job step that contains a mix of small and large programs wastes ...
ISAM was replaced at IBM with a methodology called VSAM (virtual storage access method). Still later, IBM developed SQL/DS and then Db2 which IBM promotes as their primary database management system. VSAM is the physical access method used in Db2. [citation needed]
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 ...