Abstract

Periodic-frequent patterns (or itemsets) are an important class of regularities that exist in a transactional database. Finding these patterns involves discovering all frequent patterns that satisfy the user specified maximum periodicity constraint. This constraint controls the maximum inter-arrival time of a pattern in a database. The time complexity to measure periodicity of a pattern is O(n), where n represents the number of timestamps at which the corresponding pattern has appeared in a database. As unusually represents a high value in voluminous databases, determining the periodicity of every candidate pattern in the itemset lattice makes the periodic-frequent pattern mining a computationally expensive process. This paper introduces a novel approach to address this problem. Our approach determines the periodic interestingness of a pattern by adopting greedy search. The basic idea of our approach is to discover all periodic-frequent patterns by eliminating a periodic patterns based on suboptimal solutions. The best and worst case time complexities of our approach to determine the periodic interestingness of a frequent pattern are O(1) and O(n), respectively. We introduce two pruning techniques and propose a pattern-growth algorithm to find these patterns efficiently. Experimental results show that our algorithm is runtime efficient and highly scalable as well.

Abstract

Online advertising provides an opportunity for product sellers and service providers to reach customers and has become a key factor in the growth of economy. It is a major source of revenue for the major search engine and social networking sites. Search engine, context-specific and banner advertising are the major modes of online advertising. The banner advertisement mode has certain advantages over other modes of advertising. Currently, the number of websites registered comes to a billion. Each day, a typical website receives the number of visitors ranging from hundreds to millions. In a few years, the entire population of the globe is going to be connected to Internet and browse websites. It is possible for a product seller or service provider to reach every potential customer through banner advertising. In this paper, a framework is proposed to harvest the pages views of web by forming the clusters of similar websites. Rather than managing a single website, the publisher manages the aggregated advertising space of a collection of websites. As a result, the advertisement space could be expanded significantly and it will provide the opportunity for increased number of publishers to market the aggregated advertisement space of millions of websites to advertisers for reaching potential customers. It will also help in balancing the management of banner advertising market.

Abstract

We propose a model of coverage patterns (CPs) and approaches for extracting CPs from transactional databases. The model is motivated by the problem of banner advertisement placement in e-commerce web sites. Normally, an advertiser expects that the banner advertisement should be displayed to a certain percentage of web site visitors. On the other hand, to generate more revenue for a given web site, the publisher makes efforts to meet the coverage demands of multiple advertisers. Informally, a CP is a set of non overlapping items covered by certain percentage of transactions in a transactional database.The CPs do not satisfy the downward closure property. Efforts are being made in the literature to extract CPs using level-wise pruning approach. In this paper, we propose CP extraction approaches based on pattern growth techniques. Experimental results show that the proposed pattern growth approaches improve the performance over the level-wise pruning approach. The results also show that CPs could be used in meeting the demands of multiple advertisers.

Abstract

Protein-protein interaction (PPI) networks are valuable biological data source which contain rich information useful for protein function prediction. The PPI network data set obtained from high-throughput experiments is known to be noisy and incomplete. By modeling PPI data as a graph, research efforts are being made in the literature to improve the performance of protein function prediction by extending common neighbor, clustering, and classification based approaches. These approaches exploit the fact that protein shares function with other proteins which are connected through common neighbours. As PPI data is modeled as a graph, it contains prominent nodes which establish relatively high connectivity with other modes. In this paper we propose an improved approach for protein function prediction by exploiting the connectivity properties of prominent proteins. Experimental results on real-world data sets demonstrate the effectiveness of proposed approach.

Abstract

The system of advertising on web search engines is more popularly known as Adwords. In Adwords, advertisers bid on relevant search keywords for displaying their advertisement when a query containing any of those keywords is fired. During keyword auction, there is very high competition for the frequent keywords while little to no competition for the less frequent ones. In this paper, we have proposed an approach to utilize the eye balls related to infrequent keywords to meet the demands of more advertisers by employing the notions of coverage and concept taxonomy. We employed the notion of coverage to form the multiple distinct groups of infrequent keywords. We also employed concept taxonomy to ensure that each group of keywords is semantically related. We have conducted experiments on the search queries dataset of AOL search engine. The results show that the proposed approach has a potential to meet the advertising demands of more number of advertisers over the existing approach.

Abstract

Protein-protein interaction (PPI) networks are valuable biological data source which contain rich information useful for protein function prediction. The PPI network data obtained from high-throughput experiments is known to be noisy and incomplete. In the literature, common neighbor, clustering, and classification-based approaches have been proposed to improve the performance of protein function prediction by modeling PPI data as a graph. These approaches exploit the fact that protein shares function with other proteins directly interacting with it. In this paper we have experimented an alternative approach by exploiting the notion that two proteins share a function if they have a well defined group of directly or indirectly connected common neighbors. The experiments conducted on variety of PPI network datasets show that the proposed approach improves protein function prediction accuracy over existing approaches

Abstract

The systems for weather observation and forecast are being operated to deal with adverse weather in general to mankind. Weather-based decision support systems (DSSs) are being build to improve the efficiency of the production systems in the domains of health, agriculture, livestock, transport, business, planing, governance and so on.The weather-based DSS provides appropriate suggestions based on the weather condition of the given period for the selected domain. In the literature, the notion of reuse is being employed in improving the efficiency of DSSs. In this paper, we have proposed a framework to identify similar weather conditions, which could help in improving the performance of weather-based DSSs with better reuse. In the proposed framework, the range of weather variable is divided into categories based on its influence on that domain. We form a weather condition for a period which is the combination of category values of weather variables. By comparing the daily/weekly weather conditions of a given year to weather conditions of subsequent years, the proposed framework identifies the extent of reuse.We have conducted the experiment by applying the proposed framework on 30 years of weather data of Rajendranagar, Hyderabad and using the categories employed by India Meteorological Department in Meteorology domain. The results show that there is a significant degree of similarity among daily and weekly weather conditions over the years. The results provide an opportunity to improve the efficiency of weather-based DSSs by improving the degree of reuse of the developed suggestions/knowledge for the corresponding weather conditions.

Abstract

The systems for weather observation and forecast are being operated to deal with adverse weather in general to mankind. Weatherbased decision support systems (DSSs) are being build to improve the efficiency of the production systems in the domains of health, agriculture, livestock, transport, business, planing, governance and so on. The weather-based DSS provides appropriate suggestions based on the weather condition of the given period for the selected domain. In the literature, the notion of reuse is being employed in improving the efficiency of DSSs. In this paper, we have proposed a framework to identify similar weather conditions, which could help in improving the performance of weather-based DSSs with better reuse. In the proposed framework, the range of weather variable is divided into categories based on its influence on that domain. We form a weather condition for a period which is the combination of category values of weather variables. By comparing the daily/weekly weather conditions of a given year to weather conditions of subsequent years, the proposed framework identifies the extent of reuse. We have conducted the experiment by applying the proposed framework on 30 years of weather data of Rajendranagar, Hyderabad and using the categories employed by India Meteorological Department in Meteorology0 domain. The results show that there is a significant degree of similarity among daily and weekly weather conditions over the years. The results provide an opportunity to improve the efficiency of weather-based DSSs by improving the degree of reuse of the developed suggestions/knowledge for the corresponding weather conditions.

Abstract

Protein-protein interaction (PPI) networks are valuable biological source of data which contain rich information useful for protein function prediction. The PPI networks face data quality challenges like noise in the form of false positive edges and incompleteness in the form of missing biologically valued edges. These issues can be handled by enhancing data quality through graph transformations for improved protein function prediction. We proposed an improved method to extract similar proteins based on the notion of relaxed neighborhood. The proposed method can be applied to carry out graph transformation of PPI network data sets to improve the performance of protein function prediction task by adding biologically important protein interactions, removing dissimilar interactions and increasing reliability score of the interactions. By preprocessing PPI network data sets with the proposed methodology, experiment results on both un-weighted and weighted PPI network data sets show that, the proposed methodology enhances the data quality and improves prediction accuracy over other approaches. The results indicate that the proposed approach could utilize underutilized knowledge, such as distant relationships embedded in the PPI graph.

Abstract

The process of frequent pattern extraction finds interesting information about the association among the items in a transactional database. The notion of support is employed to extract the frequent patterns. Normally, in a given domain, a set of items can be grouped into a category and a pattern may contain the items which belong to multiple categories. In several applications, it may be useful to distinguish between the pattern having items belonging to multiple categories and the pattern having items belonging to one or a few categories. The notion of diversity captures the extent the items in the pattern belong to multiple categories. The items and the categories form a concept hierarchy. In the literature, an approach has been proposed to rank the patterns by considering the balanced concept hierarchy. In a real life scenario, the concept hierarchies are normally unbalanced. In this paper, we propose a general approach to calculate the rank based on the diversity, called drank, by considering the unbalanced concept hierarchy. The experiment results show that the patterns ordered based on drank are different from the patterns ordered based on support, and the proposed approach could assign the drank to different kinds of unbalanced patterns