The following operators can be used to better focus your queries.
( ) , AND, OR, NOT, W/#
? single char wildcard, not at start
* multi char wildcard, not at start
(Cat? OR feline) AND NOT dog?
Cat? W/5 behavior
(Cat? OR feline) AND traits
Cat AND charact*
This guide provides a more detailed description of the syntax that is supported along with examples.
This search box also supports the look-up of an IP.com Digital Signature (also referred to as Fingerprint); enter the 72-, 48-, or 32-character code to retrieve details of the associated file or submission.
Concept Search - What can I type?
For a concept search, you can enter phrases, sentences, or full paragraphs in English. For example, copy and paste the abstract of a patent application or paragraphs from an article.
Concept search eliminates the need for complex Boolean syntax to inform retrieval. Our Semantic Gist engine uses advanced cognitive semantic analysis to extract the meaning of data. This reduces the chances of missing valuable information, that may result from traditional keyword searching.
Illustrating confidence measures graphically for a segmentation model in data mining
English (United States)
This text was extracted from a PDF file.
100% of the total text.
Page 1 of 1
Illustrating Confidence Measures Graphically for a Segmentation Model in Data Mining
During the model build process a set of histogram bins are maintained by the system. These bins are displayed graphically at the end of each training pass allowing the analyst to understand the distribution of confidence measures inside each cluster or segment in the model. Traditional data mining programs rely on only displaying a score or error value for the closeness of a cluster in the model. This method in addition to the score displays an easy to view distribution of scores, it is easy to understand how tightly bound the scores are inside each cluster, where as a single number does not obviously provide the same visual impact. Equally, splits in the distribution can have important implication in interpreting the results of the clustering. The total range of scores is 0 to 1 this is divided into ten even cells and the count of records falling into each range is maintained. The output is displayed as a histogram. This uses a graphical method rather than a single number.