This article presents a profile-based authorship analysis method which first categorizes texts according to social and conceptual characteristics of their author (e.g. Sex and Political Ideology) and then combines these profiles for two authorship analysis tasks: (1) determining shared authorship of pairs of texts without a set of candidate authors and (2) clustering texts according to characteristics of their authors in order to provide an analysis of the types of individuals represented in the data set. The first task outperforms Burrows' Delta by a wide margin on short texts and a small margin on long texts. The second task has no such benchmark with existing methods. The data set for evaluating the method consists of speeches from the US House and Senate from 1995 to 2013. This data set contains both a large number of texts (42,000 in the test sets) and a large number of speakers (over 800). The article shows that this approach to authorship analysis is more accurate than existing approaches given a data set with hundreds of authors. Further, this profile-based method makes new types of analysis possible by looking at types of individuals as well as at specific individuals.

Interface design is one of the most important topics during web development process. The final design is a tradeoff between the owner's personal idea and the web developer's perception of what he wants. In this paper, we have proposed a new model called WLDM (Web layout design model) to cover the important components of interface design. There are three components in the WLDM, including structure, content and visual. We have selected three features for structure, two for content, and three for visual component. Thereafter, we have made a dataset using 1088 most visited web sites. Finally, applying K-means algorithm, we have clustered this dataset. According to our result, six clusters were identified. Considering WLDM, web layout designer have a blueprint to cover areas of research related to this issue. The result of this clustering can be used for recommender systems to map owner groups, which have different attitude.

Currently providers are trying to personalize their websites according to user profiles. With respect to the wide variety and great volume of websites, providers look for a design that is more attractive than that of competitors. They look for a unique solution. In this uniqueness, any point such as design, userfriendliness, and content offered to the customer plays a key role in its success. The main objective of this study is to provide profiles of different kinds of users. Later on, this information can be used to design appropriate websites. This kind of information can be explored from social networks. We obtained a dataset of 500 users and we have clustered this dataset to 12 clusters, and then applied Collaborative Filtering on user data to improve the results. The paper will present the corresponding results and provide an interesting overview of different profiles of users in different parts of the world.