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Conscious care behaviourAwareness Risk Organization policy a b s t r a c t Today, the Internet can be considered to be a basic commodity, similar to electricity, without which many businesses simply cannot operate. However, information security for both private and business aspects is important. Experts believe that technology cannot solely guarantee a secure environment for information. Users' behaviour should be considered as an important factor in this domain. The Internet is a huge network with great potential for information security breaches. Hackers use different methods to change confidentiality, integrity, and the availability of information in line with their benefits, while users intentionally or through negligence are a great threat for information security. Sharing their account information, downloading any software from the Internet, writing passwords on sticky paper, and using social security numbers as a username or password are examples of their mistakes. Users' negligence, ignorance, lack of awareness, mischievous, apathy and resistance are usually the reasons for security breaches. Users' poor information security behaviour is the main problem in this domain and the presented model endeavours to reduce the risk of users' behaviour in this realm. The results of structural equation modelling (SEM) showed that Information Security Awareness, Information Security Organization Policy, Information Security Experience and Involvement, Attitude towards information security, Subjective Norms, Threat Appraisal, and Information Security Self-efficacy have a positive effect on users' behaviour. However, Perceived Behavioural Control does not affect their behaviour significantly. The Protection Motivation Theory and Theory of Planned Behaviour were applied as the backbone of the research model. ScienceDirect jo urnal homepage: www .e lsev ie r. co m/ lo cate/ co se c o m p u t e r s & s e c u r i t y 5 3 ( 2 0 1 5 ) 6 5 e7 8

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Abstract.Clustering is an essential data mining and tool for analyzing big data. There are difficulties for applying clustering techniques to big data duo to new challenges that are raised with big data. As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the question is how to cope with this problem and how to deploy clustering techniques to big data and get the results in a reasonable time. This study is aimed to review the trend and progress of clustering algorithms to cope with big data challenges from very first proposed algorithms until today's novel solutions. The algorithms and the targeted challenges for producing improved clustering algorithms are introduced and analyzed, and afterward the possible future path for more advanced algorithms is illuminated based on today's available technologies and frameworks.

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