Data classification is a challenging task in era of big data due to high number of features. Feature selection is a step in process of knowledge discovery in data that aims to reduce dimensionality and improve the classification performance. The purpose of this research is to define new techniques for feature selection in order to improve classification accuracy and reduce the time required for...

Data mining is used to extract useful information from the large amount of data. It is used to implement and solve different types of research problems. The research related areas in data mining are text mining, web mining, image mining, sequential pattern mining, spatial mining, medical mining, multimedia mining, structure mining and graph mining. Text mining also referred to text of data mini...

Text Mining is an important step of Knowledge Discovery process. It is used to extract hidden information from not-structured or semi-structured data. This aspect is fundamental because most of the Web information is semistructured due to the nested structure of HTML code, is linked and is redundant. Web Text Mining helps whole knowledge mining process in mining, extraction and integration of u...

In many contexts today, documents are available in a number of versions. In addition to explicit knowledge that can be queried/searched in documents, these documents also contain implicit knowledge that can be found by text mining. In this paper we will study association rule mining of temporal document collections, and extend previous work within the area by 1) performing mining based on seman...

The KD in FM project aims to investigate how Knowledge Discovery in Databases (KDD), and particularly data mining, techniques can be applied to the distributed, heterogeneous and autonomous data sources found in the Facilities Management (FM) environment. The problems associated with multiple disparate databases are examined as is recent research in heterogeneous database mining. Finally, we de...

Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from machine learning, natural language processing (NLP), data mining, information retrieval (IR), and knowledge management. Text mining involves the pre-processing of document collections such as information extraction, term extraction, text categorization, and storage of in...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

In many contexts today we have documents available in a number of versions. In addition to explicit knowledge that can be queried/searched in documents, these documents also contain implicit knowledge that can be found by text mining. In this paper we will study association rule mining of temporal document collections, and extend our previous work by 1) performing mining based on semantics as w...

The current DSS tools are generally built as “desktop applications” and designed for the use of data mining experts. In this paper, design and implementation of ASMINER, a new web-based data mining exploration and reporting tool, is introduced. ASMINER enables both decision makers and also knowledge workers, exploring and reporting with three data mining techniques (decision trees, clustering a...

As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The D...