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In the recent years, researchers in IT and business have become interested in Enterprise architecture (EA) increasingly. One reason for this growing interest is the wide range of benefits associated with EA. The review's main goals are to examine the status and progress of EA research in the world from 2005 to 2014; to provide other researchers with an extensive review which makes their paths easier in relevant studies; and to propose areas for future studies within this area. Analyses are carried out on the papers published in various journals during 10 years in EA studies. 120 papers were collected. For each article, the paper investigates distribution of collected papers in terms of time, journal, topics, author's institutional affiliation, university and country. Findings suggested that EA is receiving more attention from scholars over time and the number of published papers peaked in 2012. A significant amount of papers kept focused on modeling and usage. Furthermore, the main contributing factor to EA is university and the majority of papers belong to the United States of America (the U.S.A), followed by Sweden. The journal of Information Systems and e-Business Management published most papers in EA. Based on reviewing the existing literature, there are very few studies focusing on reviewing EA and one could not find any systematic literature review in the field of Enterprise Architecture during the last decade; therefore, to bridge the gap we decided to conduct this systematic literature review for Enterprise architecture.

Feature ranking is one of the basic methods in feature selection to select a subset of the original features. This paper uses a fuzzy clustering algorithm and proposes a new criterion for ranking the features. The importance of features is evaluated via the density function that is calculated in subtractive clustering. The proposed algorithm is tested over several well-known benchmark datasets. The performance of the proposed algorithm is also compared with some common algorithms. The results show that the proposed method is comparable to the other methods in term of obtained classification accuracy.