Search results
Results from the WOW.Com Content Network
Data classification is the process of organizing data into categories based on attributes like file type, content, or metadata. The data is then assigned class labels that describe a set of attributes for the corresponding data sets. The goal is to provide meaningful class attributes to former less structured information.
The first step in doing a data classification is to cluster the data set used for category training, to create the wanted number of categories. An algorithm, called the classifier, is then used on the categories, creating a descriptive model for each. These models can then be used to categorize new items in the created classification system. [2]
Data classification may refer to: Data classification (data management) Data classification (business intelligence) Classification (machine learning), classification of data using machine learning algorithms; Assigning a level of sensitivity to classified information; In computer science, the data type of a piece of data
Statlog (German Credit Data) Binary credit classification into "good" or "bad" with many features Various financial features of each person are given. 690 Text Classification 1994 [416] H. Hofmann Bank Marketing Dataset Data from a large marketing campaign carried out by a large bank . Many attributes of the clients contacted are given.
Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!
Pages in category "Data processing" The following 48 pages are in this category, out of 48 total. ... Data analysis; Data classification (business intelligence) Data ...
a framework to organize and analyze data, [7] a framework for enterprise architecture. [8] a classification system, or classification scheme [9] a matrix, often in a 6x6 matrix format; a two-dimensional model [10] or an analytic model. a two-dimensional schema, used to organize the detailed representations of the enterprise. [11]
As most tree based algorithms use linear splits, using an ensemble of a set of trees works better than using a single tree on data that has nonlinear properties (i.e. most real world distributions). Working well with non-linear data is a huge advantage because other data mining techniques such as single decision trees do not handle this as well.