Most studies of data mining have focus on structured data such as relational, transactional, and data warehouse data. However, the most available information is stored in text database, which consist of large amounts of text documents such as news articles, research papers, and e-mail messages. Data stored in most text databases are unstructured data, such as abstract and contents. The ability ...

This paper proposes a method that predicts attractive evaluation objects. In the learning phase, the method inductively acquires trend rules from complex sequential data. The data is composed of two types of data. One is numerical sequential data. Each evaluation object has respective numerical sequential data. The other is text sequential data. Each evaluation object is described in texts. The...

We follow an empirical approach from data quality toward text quality, where the expectations of the consumer, human or machine, take the centre stage. We try to obtain numerical text quality statements which must be interpreted for the expectations of the user and suitability for automatic natural language processing (NLP) separately. We state that apart from text accessibility today only repr...

We present an implemented system for processing definite descriptions. The system is based on the results of a corpus analysis previously reported, which showed how common discourse-new descriptions are in newspaper corpora, and identified several problems to be dealt with when developing computational methods for interpreting bridging descriptions. The annotated corpus produced in this earlier...

Software linguistics is the science of software languages. In this short paper, we sketch the general discipline of software linguistics, but our focus is on one part of it: empirical analysis of software languages. Such analysis is concerned with understanding language usage on the grounds of a corpus. In this short paper, we sketch a survey on empirical language analysis, and we argue that th...

This research proposes a new strategy where documents are encoded into string vectors and modified version of KNN to be adaptable to string vectors for text categorization. Traditionally, when KNN are used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in...

This research proposes a new strategy where documents are encoded into string vectors and modified version of k means algorithm to be adaptable to string vectors for text clustering. Traditionally, when k means algorithm is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classifi...

BACKGROUND Although real-time tracking of moving objects using a variety of sensor parameters is in great demand in monitoring systems, no studies have reported α-[Formula: see text]-[Formula: see text] tracking filters using simultaneous measurements including acceleration. In this report, we propose and analyze two α-[Formula: see text]-[Formula: see text] filters using acceleration measureme...

In this work, we construct the lumped Galerkin approach based on cubic B-splines to obtain the numerical solution of the generalized regularized long wave equation. Applying the von Neumann approximation, it is shown that the linearized algorithm is unconditionally stable. The presented method is implemented to three test problems including single solitary wave, interaction of two solitary wave...