through the loud indexed lists and experiencing unimportant query items. Hence a system is initiated using digital textual data mining standards for configuration and execution, which enhances IIR (Intelligent Information Retrieval) viability in digital forensics. The framework analyzes the input corpus

organizing, visualizing, seaxching, categorizing and filtering textual data. Furthermore, we discuss and illustrate with examples how document maps can aid in these situations. An example is presented where a document map is utilized as a tool for visualizing and filtering a stream of incoming electronic

Abstract. We address in this report the problem of representing formally textual data. First, this problem is replaced in the context of automatic text processing. Then, the weaknesses of the basic document representation, i.e. the bag-of-words representation, are explained and some state

Abstract – Business intelligence systems find textual data a very useful source of information. Text processing algorithms and systems in English and other world languages are well developed, which is not the case with Croatian language. This paper explores the applicability of existing systems

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We present a new methodology for modeling and predicting future events through machine learning and data mining techniques from textual data. Modeled events span across varied domains including politics, economy and society. The model employs human-style prediction techniques such as causality

Latent Semantic Analysis of Text Information The paper presents an overview of the usage of LSA for analysis of textual data. The mathematical apparatus is explained in brief and special attention if pointed on the key parameters that influence the quality of the results obtained. The potential

This article summarizes our research to date in the automatic identification of emerging trends in textual data. Applications are numerous: the detection of trends in warranty repair claims, for example, is of genuine interest to NCSA industrial partners Caterpillar and Boeing. Technology

We present two methodologies for the detection of emerging trends in the area of textual data mining. These manual methods are intended to help us improve the performance of our existing fully automatic trend detection system [3].

Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples