Generally, decisions are based on information. To be useful, information must be reliable. Basically, the concept of a Z-number relates to the issue of reliability of information. A Z-number, Z, has two components, Z=(A,B). The first component, A, is a restriction (constraint) on the values which a real-valued uncertain variable, X, is allowed to take. The second component, B, is a measure of reliability (certainty) of the first component. Typically, A and B are described in a natural language, for example: (about 45 minutes, very sure). Z-number has many applications, especially in the realms of economics, decision analysis, risk assessment, prediction, anticipation, rule-based characterization of imprecise functions and relations, and biomedicine. Different methods, applications, and systems are discussed. Other Fuzzy concepts are also discussed.

Methods and Systems for Applications for Z-numbers

https://isiarticles.com/bundles/Article/pre/pdf/19986.pdf

A text-mining-based patent network: Analytical tool for high-technology trend

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

/pdf/effective-pattern-discovery-for-text-mining-41clz01mjq.pdf

Effective Pattern Discovery for Text Mining

A systematic approach for identifying technology opportunities: Keyword-based morphology analysis

The popularity of the Web and the large number of documents available in electronic form has motivated the search for hidden knowledge in text collections. Consequently, there is growing research interest in the general topic of text mining. In this dissertation, we develop a text-mining system by integrating methods from Information Extraction (IE) and Data Mining (Knowledge Discovery from Databases or KDD). By utilizing existing IE and KDD techniques, text-mining systems can be developed relatively rapidly and evaluated on existing text corpora for testing IE systems. 
We present a general text-mining framework called DISCOTEX which employs an IE module for transforming natural-language documents into structured data and a KDD module for discovering prediction rules from the extracted data. When discovering patterns in extracted text, strict matching of strings is inadequate because textual database entries generally exhibit variations due to typographical errors, misspellings, abbreviations, and other sources. We introduce the notion of discovering “soft-matching” rules from text and present two new learning algorithms. TEXTRISE is an inductive method for learning soft-matching prediction rules that integrates rule-based and instance-based learning methods. Simple, interpretable rules are discovered using rule induction, while a nearest-neighbor algorithm provides soft matching. SOFTAPRIORI is a text-mining algorithm for discovering association rules from texts that uses a similarity measure to allow flexible matching to variable database items. We present experimental results on inducing prediction and association rules from natural-language texts demonstrating that TEXTRISE and SOFTA PRIORI learn more accurate rules than previous methods for these tasks. We also present an approach to using rules mined from extracted data to improve the accuracy of information extraction. Experimental results demonstrate that such discovered patterns can be used to effectively improve the underlying IE method.

/pdf/text-mining-with-information-extraction-7g2sbtyl7d.pdf

Text mining with information extraction

Knowledge Discovery in Databases (KDD), also known as data mining, focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form Given a collection of text documents, most approaches to text mining perform knowledge-discovery operations on labels associated with each document At one extreme, these labels are keywords that represent the results of non-trivial keyword-labeling processes, and, at the other extreme, these labels are nothing more than a list of the words within the documents of interest This paper presents an intermediate approach, one that we call text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document These terms plus additional higher-level entities are then organized in a hierarchical taxonomy and are used in the knowledge discovery process This paper describes Document Explorer, our tool that implements text mining at the term level It consists of a document retrieval module, which converts retrieved documents from their native formats into documents represented using the SGML mark-up language used by Document Explorer; a two-stage term-extraction approach, in which terms are first proposed in a termgeneration stage, and from which a smaller set are then selected in a term-filtering stage in light of their frequencies of occurrence elsewhere in the collection; our taxonomy-creation tool by which the user can help specify higher-level entities that inform the knowledge-discovery process; and our knowledge-discovery tools for the resulting term-labeled documents Finally, we evaluate our approach on a collection of patent records as well as Reuters newswire stories Our results confirm that Text Mining serves as a powerful technique to manage knowledge encapsulated in large document collections

O. Liphsta

Papers

Knowledge Management: A Text Mining Approach