An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

An intelligent recommendation system framework for student relationship management

In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification

Terminology mining in social media

The highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. This paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. We explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. We also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining

An overview of decision table literature.

The present report contains an overview of the literature on decision tables since its origin. The goal is to analyze the dissemination of decision tables in different areas of knowledge, countries and languages, especially showing these that present the most interest on decision table use. In the first part a description of the scope of the overview is given. Next, the classification results by topic are explained. An abstract and some keywords are included for each reference, normally provided by the authors. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. Other examined topics are the theoretical or practical feature of each document, as well as its origin country and language. Finally, the main body of the paper consists of the ordered list of publications with abstract, classification and comments.

Classification Arabic Twitter User’s Insights Using Rough Set Theory

Nowadays, people using social media from around the world to share their daily affairs. Arabic twitter for example is a platform where users read, reply, post which known ‘tweets’. Users trading their opinions on different trends that are not equal in important and differed based on their power and interest. Tweets can provide rich information to make decision. The main objective of this paper is to present a framework for making a valuable decision through analyzing social users' insights based on their proximity to a particular trend with highlights their power in this trend. Tweets are exceedingly unstructured that makes it difficult to analyze. Nevertheless, our proposed model differs from previous research in this field it gathered the use of supervised and unsupervised machine learning algorithms. The process of performing this work as follows: classifying users based on the degree of their closeness/interest utilizing Mendelow’s power/interest matrix, rough set theory to eliminate the features that may be found in user profiles to find minimal sets of data. The proposed model applied two attribute reduction algorithms on our dataset to determine the optimal number of reducts for improving decision making from the user replies. In addition to, unsupervised machine learning to group their replies into subcategories such as positive, negative, or neutral. The experimental evaluation shows that Johnson algorithm has reduced the user attributes by 71% than genetic algorithm that utilized in a classification model