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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]
Analysis tools, which are used to perform statistical or process analysis; Program management tools, used to manage and track a corporation's entire Six Sigma program; DMAIC and Lean online project collaboration tools for local and global teams;
Code cleanup can also refer to the removal of all computer programming from source code, or the act of removing temporary files after a program has finished executing. For instance, in a web browser such as Chrome browser or Maxthon , code must be written in order to clean up files such as cookies and storage. [ 6 ]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...
Various plots of the multivariate data set Iris flower data set introduced by Ronald Fisher (1936). [1]A data set (or dataset) is a collection of data.In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question.
Different levels of analysis include: function level - sequences of instruction. file or class-level - an extensible program-code-template for object creation. application level - a program or group of programs that interact. The scope of the analysis determines its accuracy and capacity to detect vulnerabilities using contextual information. [8]
Besides, data organization of containers can be optimized. The typical data structure in serial BFS and some parallel BFS is FIFO Queue, as it is simple and fast where insertion and delete operation costs only constant time. Another alternative is the bag-structure. [4]