Prakash kumar

B k tripathy

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Rapid Manufacturing

In recent times, an enumerable number of clustering algorithms have been developed whose main function is to make sets of objects have almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also, some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and therefore have stability issues. Thus, handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 an MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data but cannot be used robustly for hybrid data. In this article, the authors generalize the MMeR algorithm with neighborhood relations and make it a neighborhood rough set model which this article calls MMeNR (Min Mean Neighborhood Roughness). It takes care of the heterogeneous data. Also, the authors have extended the MMeNR method to make it suitable for various applications like geospatial data analysis and epidemiology.

International Journal of Fuzzy System Applications

Clustering Hybrid Data Using a Neighborhood Rough Set Based Algorithm and Expounding its Application

In recent times enumerable number of clustering algorithms have been developed whose main function is to make sets of objects having almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and so have stability issues. Thus handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 MMR [1] algorithm was developed which was based on basic rough set theory. MMeR [2] was proposed in 2009 which surpassed the results of MMR in taking care of categorical data. It has the capability of handling heterogeneous data but only to a limited extent because it is based on classical rough set model. In this paper, we generalize the MMeR algorithm with neighbourhood relations and make it a neighbourhood rough set model which we call MMeNR (Min Mean Neighborhood Roughness). It takes care of the heterogeneous data and also the uncertainty associated with it. Standard data sets have been used to gauge its effectiveness over the other methods. © 2017 IEEE.

2017 International Conference on Inventive Communication and Computational Technologies (ICICCT)

MMeNR: Neighborhood rough set theory based algorithm for clustering heterogeneous data

Scientific study of network data can reveal many important behaviors of the elements involved and social trends. It also provides insight for suitable changes in the social structure and roles of individuals in it. There are many evidences (HIPAA (2002) Health insurance portability and accountability act. Available online http://www.hhs.gov/ocr/hipaa; Lambert, J Off Stat 9:313-331, 1993; Xu (2006) Utility based anonymisation using local recording. In: KDD'06, Philadelphia) which indicate the precious value of social network data in shedding light on social behavior, health, and well-being of the general public. For this purpose, the social network information needs to be published publicly or before a specialized group. But, depending upon the privacy model considered, this information may involve some sensitive data of individual participants in the social network, which are undesirable to be disclosed. Due to this problem, social network data need to be anonymized before its publication in order to prevent potential reidentification attacks. Data anonymization techniques are abundantly used in relational databases (Aggarwal et al. J Priv Technol, 2005; Backstrom et al. (2007) Wherefore art thou R3579X? Anonymized social networks, hidden patterns, and structural steganography. In: International world wide web conference (WWW). ACM, New York, pp 181-190; Bayardo and Agrawal (2005) Data privacy through optimal k-anonymisation. In: IEEE 21st international conference on data engineering, April 2005; Bamba et al. (2008) Supporting anonymous location queries in mobile environments with privacy grid. In: ACM world wide web conference; Byun et al. (2007) Efficient k-anonymisation using clustering techniques. In: International conference on database systems for advanced applications (DASFAA), pp 188-200; Campan and Truta (2008) A clustering approach for data and structural anonymity in social networks. In: ACM SIGKDD workshop on privacy, security, and trust in KDD (PinKDD), Las Vegas; Chakrabarti et al. (2004) R-MAT: a recursive model for graph mining. In: SIAM international conference on data mining; Chawla et al. (2005) Toward privacy in public databases. In: Proceedings of the theory of cryptography conference, Cambridge, MA; Evfimievski et al. (2003) Limiting privacy breaches in privacy preserving data mining. In: ACM principles of database systems (PODS). ACM, New York, pp 211-222; Getoor and Diehl, A surv SIGKDD Explore Newsl 7(2):3-12, 2005; Ghinita et al. (2007) Fast data anonymisation with low information loss. In: Very large data base conference (VLDB), Vienna, pp 758-769; Lefebvre et al. (2006) Mondrian multidimensional K-anonymity. In: IEEE international conference of data engineering (ICDE), p 25; Liu and Terzi (2008) Towards identity anonymisation on graphs. In: Wang (ed.) SIGMOD conference. ACM, New York, pp 93-106; Lunacek et al. (2006) A crossover operator for the k-anonymity problem. In: Genetic and evolutionary computation conference (GECCO), Seattle, Washington, pp 1713-1720; Machanavajjhala et al. (2006) L-diversity: privacy beyond K-anonymity. In: IEEE international conference on data engineering (ICDE), Atlanta, p 24; Malin, J Am Med Inform Assoc 12(1):28-34, 2004; Nergiz and Clifton (2006) Thoughts on k-anonymisation. In: IEEE 22nd international conference on data engineering workshops (ICDEW), Atlanta, April 2006, p 96; Nergiz and Clifton (2007) Multirelational k-anonymity. In: IEEE 23rd international conference on data engineering posters, April 2007). However, most of the known anonymisation approaches such as suppression or generalization do not directly apply to social network data. One major challenge in social network anonymization is the complexity. In (Gross and Yellen (2006) Graph theory and its applications. CRC, Boca Raton), it has been proved that a particular k-anonymity problem trying to minimize the structural change to the original social network is NP-hard. Research in anonymization of social networks is a relatively new field. In this chapter, we provide a systematic study of different approaches and studies done so far in this direction. There is no doubt that social network nodes can have imprecise data as their attributes. So, normal methods proposed for anonymization are not suitable for such type of social networks. Recently, a very efficient rough set-based algorithm was established in (Tripathy and Prakash Kumar, Int J Rapid Manuf 1(2):189-207, 2009) to handle clustering of tuples in relational models. We shall describe how this algorithm can be used for anonymization of social networks. Also, we shall present some recent algorithms which use isomorphism of graphs for anonymization of social networks. In the end, we shall discuss the current status of research on anonymization of social networks and present some related problems for further study. © 2012 Springer-Verlag London. All rights reserved.

Computational Social Networks

Anonymisation of Social Networks and Rough Set Approach

From the beginning of the data analysis system cluster computing plays an important role on it. The very early developed clustering algorithms which can handle only numerical data and K-means clustering is one of them and was proposed by Macqueen [1] in 1967. This algorithm helps us to find the homogeneity of the data set. This K-means algorithm has been modified in many ways to get the modified K-means and kernel based K-means is one of them. It is a nonlinear transformation which transforms the sample data into high dimensional feature space. Though this kernel based K-means performs good almost on every data set but it is unable to handle uncertainty. After rough set theory has been proposed by Pawlak [2], we have many clustering algorithms based on it which can handle uncertainty and heterogeneous data and Rough based K-means is one of them. So in this paper we are proposing the combination of these two methods and known as kernel based K-Means using rough set. © 2012 IEEE.

2012 International Conference on Computer Communication and Informatics

Kernel based K-means clustering using rough set

Use of published organizational data for a variety of purposes has the chance of violation of leakage of individual secret information. Though this is taken care by the organizations by removal or encryption of explicit identifiers, valuable information may still be leaked by quasi identifiers in the released data. The concept of k-anonymity was introduced and several algorithms in this direction have been proposed by different researchers [1, 2, 3, 4, 5, 6, 7, 8, 11] to handle this problem. But the notion of k-anonymity is susceptible to two types of attacks which necessitated the requirement of a better privacy preserving notion leading to the proposal of l- diversity [6]. In [14] a third phase is added to the two phase clustering-based k-anonymisation algorithm OKA [4] to achieve l-diversity. Recently, the clustering stage of the algorithm has been improved in [14] and the diversity stage algorithm is improved in [16] to come up with a fast l-diversity algorithm which deals with a single sensitive attribute in a relational table. Our main contribution in this paper is to develop an l-diversity algorithm to handle multi-sensitive attributes in databases. Also, we shall improve the adjustment stage algorithm so that it becomes more efficient. We also analyse and provide enough reasons to show that though the second and third stages of the algorithm are not necessary in most of the cases, we cannot avoid using these two stages in some cases. © 2011 IEEE.

Journal	International Journal of Rapid Manufacturing
Publisher	Inderscience Publishers
ISSN	1757-8817
Open Access	0