Keivan Kianmehr

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.

Calling communities analysis and identification using machine learning techniques

The analysis of social communities related logs has recently received considerable attention for its importance in shedding light on social concerns by identifying different groups, and hence helps in resolving issues like predicting terrorist groups. In the customer analysis domain, identifying calling communities can be used for determining a particular customers value according to the general pattern behavior of the community that the customer belongs to; this helps the effective targeted marketing design, which is significantly important for increasing profitability. In telecommunication industry, machine learning techniques have been applied to the Call Detail Record (CDR) for predicting customer behavior such as churn prediction. In this paper, we pursue identifying the calling communities and demonstrate how cluster analysis can be used to effectively identify communities using information derived from the CDR data. We use the information extracted from the cluster analysis to identify customer calling patterns. Customers calling patterns are then given to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply different machine learning techniques to build classifier models and compare them in terms of classification accuracy and computational performance. The reported test results demonstrate the applicability and effectiveness of the proposed approach.

Promoting where, when and what? An analysis of web logs by integrating data mining and social network techniques to guide ecommerce business promotions

The rapid development of the internet introduced new trend of electronic transactions that is gradually dominating all aspects of our daily life. The amount of data maintained by websites to keep track of the visitors is growing exponentially. Benefitting from such data is the target of the study described in this paper. We investigate and explore the process of analyzing log data of website visitor traffic in order to assist the owner of a website in understanding the behavior of the website visitors. We developed an integrated approach that involves statistical analysis, association rules mining, and social network construction and analysis. First, we analyze the statistical data on the types of visitors that come to the website, as well as the steps they take to reach and satisfy the goal of their visit. Second, we derive association rules in order to identify the correlations between the web pages. Third, we study the links between the web pages by constructing a social network based on the frequency of access to the web pages such that two web pages get linked in the social network if they are identified as frequently accessed together. The value added from the overall analysis of the website and its related data should be considered valuable for ecommerce and commercial website owners; the owners will get the information needed to display targeted advertisements or messages to their customers. Such an automated approach gives advantage to its users in the current competitive cyberspace. In the long run, this is expected to allow for the increase in sales and overall customer loyalty.

Data warehouse architecture and design

A data warehouse is attractive as the main repository of an organization’s historical data and is optimized for reporting and analysis. In this paper, we present a data warehouse the process of data warehouse architecture development and design. We highlight the different aspects to be considered in building a data warehouse. These range from data store characteristics to data modeling and the principles to be considered for effective data warehouse architecture.

Fuzzy clustering-based discretization for gene expression classification

This paper presents a novel classification approach that integrates fuzzy class association rules and support vector machines. A fuzzy discretization technique based on fuzzy c-means clustering algorithm is employed to transform the training set, particularly quantitative attributes, to a format appropriate for association rule mining. A hill-climbing procedure is adapted for automatic thresholds adjustment and fuzzy class association rules are mined accordingly. The compatibility between the generated rules and fuzzy patterns is considered to construct a set of feature vectors, which are used to generate a classifier. The reported test results show that compatibility rule-based feature vectors present a highly- qualified source of discrimination knowledge that can substantially impact the prediction power of the final classifier. In order to evaluate the applicability of the proposed method to a variety of domains, it is also utilized for the popular task of gene expression classification. Further, we show how this method provide biologists with an accurate and more understandable classifier model compared to other machine learning techniques.

Fuzzy association rule mining framework and its application to effective fuzzy associative classification

Classification is a technique widely and successfully used for prediction, which is one of the most attractive features of data mining. However, building the classifier is the most challenging part of the process, which proceeds into testing the classifier to check its effectiveness. This article introduces a classification framework that integrates fuzzy association rules into the learning process of machine learning techniques. The integrated framework involves three major components. First, we employ multiobjective optimization twice to decide on the fuzzy sets and then optimize their ranges to extract a set of interesting fuzzy association rules. Second, we use a special subset of the extracted fuzzy association rules, namely, fuzzy class association rules, for building a set of new feature vectors that measure the compatibility between the rules and the given data objects. Third, we train a classifier on the generated feature vectors to predict the class of unseen objects. Most of the earlier algorithms proposed for mining fuzzy association rules assume that the fuzzy sets are given. However, the fuzzy association rule mining component of the proposed framework uses an automated method for autonomous mining of both fuzzy sets and fuzzy association rules. For this purpose, first fuzzy sets are constructed by using a multiobjective genetic algorithm based clustering method for determining and optimizing the membership functions of the fuzzy sets. Then, a method is applied to extract interesting fuzzy association rules. Further, the proposed framework integrates a new layer to the learning process of the machine learning algorithm by constructing the compatibility rule‐based feature vectors; this satisfies the aim of better understandability. Once used by the learning algorithm, the compatibility feature vectors represent a rich source of discrimination knowledge that can substantially impact the prediction power of the final classifier. The experimental study and the reported results show the efficiency and effectiveness of our framework for benchmark datasets. In order to further demonstrate and evaluate the applicability of the proposed method to a variety of domains, it is utilized for the task of gene expression classification as well.

CARSVM: A class association rule-based classification framework and its application to gene expression data

OBJECTIVE In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms. METHOD In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well. RESULTS We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model. CONCLUSION From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.

EFFECTIVENESS OF SUPPORT VECTOR MACHINE FOR CRIME HOT-SPOTS PREDICTION

Crime hot-spot location prediction is important for public safety. The output from the prediction can provide useful information to improve the activities aimed at detecting and preventing safety and security problems. Location prediction is a special case of spatial data mining classification. For instance, in the public safety domain, it may be interesting to predict location(s) of crime hot spots. In this study, we present a support vector machine (SVM)-based approach to predict the location as an alternative to existing modeling approaches. Support vector machine forms the new generation of machine-learning techniques used to find optimal separability between classes within datasets. We compare the performance of two types of SVMs techniques: two-class SVMs and one-class SVMs. We also compared SVM with a neural network-based approach and spatial auto-regression-based approach. Experiments on two different spatial datasets demonstrate that the former approach performs slightly better and the latter one gives reasonable results. Furthermore, in this study, we provide a general framework to customize the spatial data classification task for other spatial domains that have datasets similar to the analyzed crime datasets.

NetDriller: A Powerful Social Network Analysis Tool

The social network methodology has gained considerable attention recently. The main motivation is to construct and analyze social networks that involve actors from a specific application domain. The advanced computing technology has facilitated automating the process and provided flexibility, robustness and scalability. A large number of automated tools exist. Each tool supports specific functions in addition to the general common functions inspired from the social network methodology. After identifying some of the interesting functions lacking in the existing tools, we have developed Net Driller as a powerful tool with distinguished capabilities. Compared to the existing tools, Net Driller supports some unique tasks, such as network construction based on data analysis by mining the raw dataset to produce more informative links between actors. Net Driller also facilitates fuzzy search on network metrics. In this demo paper, we introduce the basic features of Net Driller by focusing on the two functionalities mentioned above.

Employing Structural and Textual Feature Extraction for Semistructured Document Classification

This paper addresses XML document classification by considering both structural and content-based features of the documents. This approach leads to better constructing a set of informative feature vectors that represents both structural and textual aspects of XML documents. For this purpose, we integrate soft clustering of words and feature reduction into the process. To extract structural information, we employ an existing frequent tree-mining algorithm combined with an information gain filter to retrieve the most informative substructures from XML documents. However, for extracting content information, we propose soft clustering of words using each cluster as a textual feature. We have conducted extensive experiments on a benchmark dataset, namely 20NewsGroups, and an XML documents dataset given in LOGML that describes the web-server logs of user sessions. With regards to the classifier built only using our textual features, the results show that it outperforms a naive support-vector-machine (SVM)-based classifier, as well as an information retrieval classifier (IRC). We further demonstrate the effectiveness of incorporating both structural and content information into the process of learning, by comparing our classifier model and several XML document classifiers. In particular, by applying SVM and decision tree algorithms using our feature vector representation of XML documents dataset, we have achieved 85.79% and 87.04% classification accuracy, respectively, which are higher than accuracy achieved by XRules, a well-known structural-based XML document classifier.