• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.85-94
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• 2013

With the emergence of world wide web, in particular web 2.0 the rapidly growing amount of news articles has created a problem for users in selection of news articles according to their requirements. To overcome this problem different clustering mechanism has been proposed to broadly categorize news articles. However these techniques are totally machine oriented techniques and lack users' participation in the process of decision making for membership of clustering. In order to overcome the issue of zero-participation in the process of clustering news articles in this paper we have proposed a framework for clustering news articles by combining users' judgments that they post on twitter with the news articles to cluster the objects. We have employed twitter hash-tags for this purpose. Furthermore we have computed the credibility of users' based on frequency of retweets for their tweets in order to enhance the accuracy of the clustering membership function. In order to test performance of proposed methodology, we performed experiments on tweets messages tweeted during general election 2013 in Pakistan. Our results proved over claim that using users' output better outcome can be achieved then ordinary clustering algorithms.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.251-260
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• 2008

In internet being a repository of massive information, we easily may not find our desired information, this issue also exists in e-commerce which gets rapid growth. In most of e-commerce sites, the methods furnishing information have been made use of statistical analysis or simple process by category-oriented, but these can't represent diverse correlation among products information and also hardly reflect users' purchasing patterns precisely. In this thesis, we propose more efficient web mining ways for discovering interesting information using effective clustering in e-commerce, which get achieved more suitable relationship among products information using both sequential patterns and association rules in category-independent, and experiments show the efficiency of our proposed methods. And we propose search using effective clustering rapidly.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.10-19
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• 2002

In searching and locating a collection of components on the Web, users require a Web browser. Since the Web libraries tend to grow rapidly, there needs to be an effective way to organize and manage such large libraries. Traditional Web-based library(retrieval) systems provide various classification scheme and retrieval services to store and retrieve components. However, these systems do not include invaluable services, for example, enabling users to grasp the overall contents of the library at the beginning of retrieval. This paper discusses a Web-based library system, which provides the efficient management of object-oriented components and a set of services beyond simple component store and retrieval. These services consist of component comprehension through a reverse engineering process, automated summary extraction, and comprehension-based retrieval. Also, The performance of an automated cluster-based classification scheme adopted on the system is evaluated and compared with the cluster-based classification scheme adopted on the system is evaluated and compared with the performance of two other systems using traditional classification scheme.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.154-159
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• 2015

Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users' POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the user's activity label extracted from the log data is then used to learn the patterns of the user's daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the user's time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.