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Volume-1, Issue-10 September 18, 2014
Volume-1, Issue-10 September 18, 2014

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S. No

Volume-1 Issue-10, September 2014, ISSN: 2347-6389 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.



Sukhbir Singh, Dharmender Kumar

Paper Title:

Frequent Pattern Mining Algorithms: A Review

Abstract: Mining frequent patterns is one of the most important concepts of data mining. Frequent pattern mining has been a highly concerned field of data mining for researcher for over two decades. Several algorithms have been developed for finding frequent itemsets from the databases. The efficiency of these algorithms is a major issue since a long time and has captured the interest of a large community of researchers. In Literature review it is found that great effort has been made in this area so far to development of efficient and scalable algorithms for frequent itemset mining in various types of databases due to their importance in various fields. In 1993, R. Agrawal and R. Srikant first proposed the most classical association rule mining algorithm named as Apriori algorithm. But Apriori has two major drawbacks: large number of candidate itemsets generation and large no of database scan. Like most of the association rule algorithms, first it discover minimal frequent itemsets, then it discover the maximal frequent itemsets by using these minimal frequent itemsets, so all approach of this type take large time to find maximal frequent itemsets and needed large number of database scan, also not suitable for the continuous changing database. To overcome these problems, extensive work have done by many researchers, by enhancement and modification on basic algorithms like Apriori algorithm, FP growth algorithm, Eclat algorithm, and MFI algorithm etc. Maximal frequent itemset (MFI) was proposed by Bayard in the year 1998. (MFI) used to find maximal frequent item. After that lots of improved approaches have been proposed to efficiently mining the maximal frequent pattern such as Mafia, GenMax Smart-Miner etc. The present paper provides an overview of various frequent pattern mining algorithms with the expectation that it would serve as a reference material for researchers in this field.

Apriori Algorithm, Association Rules, Boolean matrix, Data Mining, Frequent Itemset, Maximal Frequent Itemset (MFI), Maximal frequent itemset first (MFIF).


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Preeti R. Dodwad, L. M. R. J. Lobo

Paper Title:

A Context-Aware Recommender System Using Ontology Based Approach for Travel Applications

Abstract: The purpose of tourism is to travel for relaxation and enjoyment. However, when tourists use internet to search for data about travel spots, events and relevant services they experience a data overload. It is also difficult for them to select what is truly interesting from sheer amount of available information. For a tourist guide system, it is still a tough task to provide proper travel information for tourists who posses different personal interests. Therefore, our aim is to develop a recommender system which considers tourists’ personal interests and related context, so that tourists can get relevant travel information with least amount of effort. This recommender system uses an ontology based approach. Ontology consists of a set of concepts relevant to a specific domain and the relationships between them. Such an ontology structure can reason depending on the choices of a user. The user profile keeps the degrees of interest of the user on many concepts by making use of a membership function. Each concept of ontology is a fuzzy set and any user can fit into this fuzzy set to a definite degree. When preliminary assignment of user choices is done, we performed an upward and downward propagation of user’s interest degrees which utilizes the taxonomical information of the ontology. The information about the user’s choices is propagated throughout the complete set of concepts. This developed system has been successfully applied for a Tourism scenario and is based on user context. This system is built on an Android platform and has generated successful results.

Context-aware recommendations, user interests, ontology, recommender systems.  


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Florence Upendo Rashidi, Senzota Kivaria Semakuwa

Paper Title:

Analysis of Rain Effect in Free Space Optical Communication under NRZ Modulation in Two Regions of Tanzania

Abstract: Free Space Optical (FSO) is an optical communication technology that uses light propagating in free space to transmit data between two points. In Tanzania now days the demand for higher and unlimited bandwidth for communication channel is highly required. For this case the communication through FSO is the best alternative solution than optical fiber. In this paper we are presenting the effects of different parameters to be used in Dodoma and Dar-es -Salaam when transmitting during the rain period. We designed a model of FSO system using OptiSystem to establish an FSO link by a range of 3 to 5 km and 5 to 15 km in Dodoma and Dar-es-Salaam respectively. In the FSO link we have used a Carbonneau model as rain attenuation model, while transmitting the data on NRZ modulation scheme, and reported analysis of various parameters like Bit Error rate (BER), transmission power and transmission length. The simulation results shows, the received signal power decrease while bit error rate increase when increasing transmission length and optical attenuation but is becoming less than 1 and less than 100 dBm respectively when transmitting within the selected range above. The analysis also found that using FSO for communication is better than optical fiber because it can avoid some challenges such as high cost of digging roads, impractical physical connection between transmitters and receivers and insecure of data.

Rain Attenuation, Free Space Optical Communications, NRZ, BER.


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Yongseung Shin, Nam Hyun Cho, Ruchire Eranga Henry Wijesinghe, Jeehyun Kim

Paper Title:

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Optical switching mechanism based SD-OCT, anterior chamber, posterior chamber.


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