Abstract: The extraction of useful information from data is often a complex process that can be conveniently modeled as a data analysis workflow. When very large data sets must be analyzed and/or complex data mining algorithms must be executed, data analysis workflows may take very long times to complete their execution. Therefore, efficient systems are required for the scalable execution of data analysis workflows, by exploiting the computing services of the Cloud platforms where data is increasingly being stored. The objective of the paper is to demonstrate how Cloud software technologies can be integrated to implement an effective environment for designing and executing scalable data analysis workflows. We describe the design and implementation of the Data Mining Cloud Framework (DMCF), a data analysis system that integrates a visual workflow language and a parallel runtime with the Software-as-a-Service (SaaS) model. DMCF was designed taking into account the needs of real data mining applica

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Abstract: As a side effect of increasingly popular social media, cyber bullying has emerged as a serious problem afflicting children, adolescents and young adults. Machine learning techniques make automatic detection of bullying messages in social media possible, and this could help to construct a healthy and safe social media environment. In this meaningful research area, one critical issue is robust and discriminative numerical representation learning of text messages. In this paper, we propose a new representation learning method to tackle this problem. Our method named Semantic-Enhanced Marginalized Denoising Auto-Encoder (smSDA) is developed via semantic extension of the popular deep learning model stacked denoising auto encoder. The semantic extension consists of semantic dropout noise and sparsity constraints, where the semantic dropout noise is designed based on domain knowledge and the word embedding technique. Our proposed method is able to exploit the hidden feature structure of bully

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Abstract: High utility itemsets (HUIs) mining is an emerging topic in data mining, which refers to discovering all itemsets having a utility meeting a user-specified minimum utility threshold min_util. However, setting min_util appropriately is a difficult problem for users. Generally speaking, finding an appropriate minimum utility threshold by trial and error is a tedious process for users. If min_util is set too low, too many HUIs will be generated, which may cause the mining process to be very inefficient. On the other hand, if min_util is set too high, it is likely that no HUIs will be found. In this paper, we address the above issues by proposing a new framework for top-k high utility itemset mining, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU (mining Top-K Utility itemsets) and TKO (mining Top-K utility itemsets in One phase) are proposed for mining such itemsets without the need to set min_util. We provide a structural comparison of the

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Abstract: Fraud is widespread and very costly to the healthcare insurance system. Fraud involves intentional deception or misrepresentation intended to result in an unauthorized benefit. It is shocking because the incidence of health insurance fraud keeps increasing every year. In order to detect and avoid the fraud, data mining techniques are applied. This includes some preliminary knowledge of health care system and its fraudulent behaviors, analysis of the characteristics of health care insurance data. Data mining which is divided into two learning techniques viz., supervised and unsupervised is employed to detect fraudulent claims. But, since each of the above techniques has its own set of advantages and disadvantages, by combining the advantages of both the techniques, a novel hybrid approach for detecting fraudulent claims in health insurance industry is proposed.

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Abstract: Data generated on location-based social networks provide rich information on the where abouts of urban dwellers. Specifically, such data reveal who spends time where, when, and on what type of activity (e.g., shopping at a mall, or dining at a restaurant). That information can, in turn, be used to describe city regions in terms of activity that takes place therein. For example, the data might reveal that citizens visit one region mainly for shopping in the morning, while another for dining in the evening. Furthermore, once such a description is available, one can ask more elaborate questions. For example, one might ask what features distinguish one region from another – some regions might be different in terms of the type of venues they host and others in terms of the visitors they attract. As another example, one might ask which regions are similar across cities. In this paper, we present a method to answer such questions using publicly shared Foursquare data. Our analysis makes use

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Abstract: With the wide application of location-based social networks (LBSNs), point-of-interest (POI) recommendation has become one of the major services in LBSNs. The behaviors of users in LBSNs are mainly checking in POIs, and these checkingin behaviors are influenced by user’s behavior habits and his/her friends. In social networks, social influence is often used to help businesses to attract more users. Each target user has a different influence on different POI in social networks. This paper selects the list of POIs with the greatest influence for recommending users. Our goals are to satisfy the target user’s service need, and simultaneously to promote businesses’ locations (POIs). This paper defines a POI recommendation problem for location promotion. Additionally, we use submodular properties to solve the optimization problem. At last, this paper conducted a comprehensive performance evaluation for our method using two real LBSN datasets. Experimental resul

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Abstract: Clustering techniques have been widely adopted in many real world data analysis applications, such as customer behavior analysis, medical data Analysis, digital forensics, etc. With the explosion of data in today’s big data era, a major trend to handle a clustering over large-scale datasets is outsourcing it to HDFS platforms. This is because cloud computing offers not only reliable services with performance guarantees, but also savings on inhouse IT infrastructures. However, as datasets used for clustering may contain sensitive information, e.g., patient health information, commercial data, and behavioral data, etc, directly outsourcing them to any Distributed servers inevitably raise privacy concerns. In this paper, we propose a practical privacy-preserving K-means clustering scheme that can be efficiently outsourced to HDFS servers.

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Abstract: In a profile matchmaking application of mobile social networks, users need to reveal their interests to each other in order to find the common interests. A malicious user may harm a user by knowing his personal information. Therefore, mutual interests need to be found in a privacy preserving manner. In this paper, we propose an efficient privacy protection and interests sharing protocol referred to as PRivacy-aware Interest Sharing and Matching (PRISM). PRISM enables users to discover mutual interests without revealing their interests. Unlike existing approaches, PRISM does not require revealing the interests to a trusted server. Moreover, the protocol considers attacking scenarios that have not been addressed previously and provides an efficient solution. The inherent mechanism reveals any cheating attempt by a malicious user. PRISM also proposes the procedure to eliminate Sybil attacks. We analyze the security of PRISM against both passive and active attacks. Thro

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Abstract: Question and Answer (Q&A) systems play a vital role in our daily life for information and knowledge sharing. Users post questions and pick questions to answer in the system. Due to the rapidly growing user population and the number of questions, it is unlikely for a user to stumble upon a question by chance that (s) he can answer. Also, altruism does not encourage all users to provide answers, not to mention high quality answers with a short answer wait time. The primary objective of this paper is to improve the performance of Q&A systems by actively forwarding questions to users who are capable and willing to answer the questions. To this end, we have designed and implemented SocialQ&A, an online social network based Q&A system. SocialQ&A leverages the social network properties of common-interest and mutual-trust friend relationship to identify an asker through friendship who are most likely to answer the question, and enhance the user security. We also improve SocialQ&A with security

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