Clustering For Similarity Search And Privacyguaranteed Publishing Of Hi-Dimensional Data Ashwini.R #1, K.Praveen *2, R.V.

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1 Clustering For Similarity Search And Privacyguaranteed Publishing Of Hi-Dimensional Data Ashwini.R #1, K.Praveen *2, R.V.Krishnaiah *3 #1 M.Tech, Computer Science Engineering, DRKIST, Hyderabad, Andhra Pradesh, India # Associate Professor, Department of CSE, DRKIST, Hyderabad, Andhra Pradesh, India # Principal, Department of CSE, DRKIST, Hyderabad, Andhra Pradesh, India Abstract--Data mining discovers knowhow required for decision making. In real world high-dimensional data is frequently used. Therefore it is essential for data mining techniques to work on high-dimensional data. Especially clustering algorithm has to work with highdimensional data. In this paper we explore the similarity search mechanisms with respect to highdimensional data. The existing techniques for indexing have certain drawbacks as they do not consider dependencies. For this reason their performance is suboptimal. In the process of clustering finding correlations of different dimensions is required. Pruning is a process of removing unnecessarydata is part of the techniques. Bounding hyper sphere and bounding rectangles are the main techniques used for pruning. They are n to efficient in Nearest Neighbor (NN) search. In this paper we proposed a novel algorithm to overcome the problem. Our technique is known as cluster-adaptive bounding which makes use of cluster based index. Our algorithm also features spatial filtering for reducing computational and storage overhead. The similarity measures such as Euclidean and Mahalanobis can also be used with our approach. We also built an application to show the proof of concept. The empirical results reveal that the proposed approach is effective with high-dimensional data for performing NN search. Keywords Data mining, high-dimensional data, similarity measures, indexing I. INTRODUCTION With new technologies invented in signal processing, it has become a common practice to process huge amount of data. In other words huge amount of data is mined in order to extract actionable knowledge. There is an increased development and the release of innovative electronic devices to process multimedia data. Apart from storage improvements the data mining has become a feasible act due to the increase in computational power of the systems. With these capabilities new technologies like GIS, CAD, and CAM came into existence besides techniques for processing medical images. Now the data in TBs is being processed. In such multimedia applications indexing the data will speed up query retrieval process. Spatial queries are used in case of high-dimensional data. Especially the queries are nearest neighbor queries. However, such queries with ED as measure are not suitable for high-dimensions data. The reason behind this is the curse of dimensionality besides the use of pessimistic metrics [1]. Search performance is to be given paramount importance in multimedia applications [2]. Prior works on this made many assumptions such as uniform data distribution and independent attributes. But in reality this is not the case with data. The data exhibits irregular data distributions besides having dependency among attributes. Such data results in the drawback curse of dimensionality [3]. Indexing such data using nearest neighbors and farthest neighbors will help in improving speed of queries. Euclidean distance measure can be used to index datasets in the real world. CBIR applications can also use such metric known as Weighted Euclidean [4]. In this paper, we throw light into the real world highdimensional datasets which are indexed for NN searches. II. RELATED WORK Indexing has been around as a popular technique to solve search problems in applications. In order to process multi-dimensional data many indexing techniques are available to help in speeding up search process. For low dimensional data R-tree structure can be used for recursive partitioning [5]. Other such indexing structures widely used include SR Tree [7], and SS-Tree [6]. There are instances where the combination of these two trees used for better ISSN: Page3711

2 performance. These are specialized in using ED as similarity measure. M-trees are very good candidates for distance functions [8]. Multidimensional indexes are used with low-dimensional spaces. They perform well when compared with sequential approaches. However, their performance is degraded when number of dimensions is increased beyond certain threshold. The sequential approach shown inferior performance. When dimensionality crosses 10, less performance is recorded with these methods due to the curse of dimensionality [9], [10] as the searchspace becomesexpone also existing that combine two or more methods. They include A-Tree and IQ-Tree. Future vectors and distance functions are used for approximations for finding similarity. The exact similarity search is achieved with approximation of perceptions with overhead in query rounds. Savings in the processing of query is done using certain strategies like MMDR [15], PAC-NN [14] and VA- LOW [16], [17]. These approaches used hashing mechanisms such as LSH (Locally Sensitive Hashing) [18]. More information is to be researched on approximation for better knowhow [19]. There are some limitations with approximate indexing as it causes tradeoffs between quality of search and the time taken to complete the search as explored in [20]. III. CLUSTER DISTANCE BOUNDING In this section estimation of distances to clusters is discussed. For making an effective cluster distance bound the following equation is used. Fig. 1 Performance comparison New approaches came into existence in order to overcome the dimensionality problem. The new technique is vector approximation file [9] which became very popular. It divides the space into small pieces known as hyper rectangles and they are subjected to quantized approximation. A new file for separate approximation is maintained in secondary storage which holds encoded data. When NN search is performed using, VA-file is searched sequentially with respect to lower and upper bounds, the distance measure is used to process the query faster. In the end the vectors from secondary storage are accessed in order to fine the nearest neighbors. With respect to VA-file the techniques which proved not appropriate include vector approximation and scalar quantization. With respect to VA-file in [11] datasets are altered for changed dimensions before they are subjected to approximation in the presence of multiple dimensions. As per the data distributions adaptive spacing is used along with approximation cells. There are many approximation techniques recently proposed in [13], [12] with a common aim of outperforming the sequential scan. Hybrid methods The distance function used for Mahalanobis distance is computed as follows. IV. CLUSTERING AND INDEX STRUCTURE Index construction is essential in case of processing queries with high-dimensional data. Such indexing is used in NN and Voronoi clusters in the real world application. Afterwards, many techniques came into existence for clustering data. The techniques include BIRCH [23], GLA [22], and fast K-means algorithm [21]. The results of algorithms are used for further processing. The results of generic clusteringalgorithms can be used further to process all pivot points. Then the dataset is scanned besides mapping of element is done with nearest pivot. Voronoi clusters are formed by mapping data to the pivot. The process is presented in algorithm 1. ISSN: Page3712

3 Algorithm 1 [24] As can be seen in algorithm 1, rearrangement of clusters is visible in order to make them more precise. Centroid is used as pivot. With the single scan results can be obtained using Voronoi clustering and with generic clustering approach. One scan is required by indexing scheme in order to take less time for making index. Algorithm 3 [24] V. EVALUATION OF RESUTLS We built a prototype application in order to demonstrate the proof of concept. Then the results are analyzed and presented in this section. The environment used to build the application includes a PC with 4 GB RAM, core 2 dual processor running Windows XP operating system. ALGORITHM 2 KNN-SEARCH (Q) [24] Fig. 2 - IO Performance of Distance Bounds (BIO- RETINA) As shown in figure 2, bio retina data set results are pages.\ ISSN: Page3713

4 Fig. 3 - IO Performance of Distance Bound (SENSORS) As shown in figure 3, SENSORS data set results are pages. Fig. 4 - IO Performance of Distance Bounds (AERIAL) As shown in figure 4, bio retina data set results are pages. Fig. 5 - IO Performance of Distance Bounds HISTOGRAM. As shown in figure 5, bio retina data set results are pages. VI. CONCLUSIONS In this paper we study indexing for high-dimensional data. Such data exhibits significant correlations and non uniform distributions. VA-file became very popular to index such data for best performance with respect to search. However, due to curse of dimensionality, this technique produces suboptimal results. In this paper we overcome this problem by proposing a new indexing method based on vector quantization. In this approach a dataset is divided into multiple voronoi clusters before further processing. Afterwards, cluster distance bounds were built using the byperplane boundaries. The new search technique can make use of distance measures like Mahalanobis and ED. The proposed indexing method also results in less IO cost and memory usage. It is also scalable and provides better performance when compared with MBS and MBR bounds. Our prototype demonstrates it and the empirical results revealed the same. REFERENCES [1] C.C. Aggarwal, A. Hinneburg, and D.A. Keim, On the Surprising Behavior of Distance Metrics in High Dimensional Spaces, Proc. Int l Conf. Database Theory (ICDT), pp , [2] B.U. Pagel, F. Korn, and C. Faloutsos, Deflating the Dimensionality Curse Using Multiple Fractal ISSN: Page3714

5 Dimensions, Proc. Int l Conf. Data Eng. (ICDE), pp , [3] K.S. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, When is Nearest Neighbor Meaningful?, Proc. Int l Conf. Database Theory (ICDT), pp , [4] J. Davis, B. Kulis, P. Jain, S. Sra, and I. Dhillon, Information- Theoretic Metric Learning, Proc. Int l Conf. Machine Learning (ICML), pp , [5] A. Guttman, R-Trees: A Dynamic Index Structure for Spatial Searching, Proc. ACM SIGMOD Int l Conf. Management of Data, pp , [6] D.A. White and R. Jain, Similarity Indexing with the SS-Tree, Proc. Int l Conf. Data Eng. (ICDE), pp , [7] N. Katayama and S. Satoh, The SR-Tree: An Index Structure for High-Dimensional Nearest Neighbor Queries, Proc. ACM SIGMOD Int l Conf. Management of Data, pp , May [8] P. Ciaccia, M. Patella, and P. Zezula, M-Tree: An Efficient Access Method for Similarity Search in Metric Spaces, Proc. Int l Conf. Very Large Databases (VLDB), pp , [9] R. Weber, H. Schek, and S. Blott, A Quantitative Analysis and Performance Study for Similarity- Search Methods in High- Dimensional Spaces, Proc. Int l Conf. Very Large Data Bases (VLDB), pp , Aug [10] R. Bellman, Adaptive Control Processes: A Guided Tour. Princeton Univ. Press, [11] H. Ferhatosmanoglu, E. Tuncel, D. Agrawal, and A.E. Abbadi, Vector Approximation Based Indexing for Non-Uniform High Dimensional Data Sets, Proc. Int l Conf. Information and Knowledge Management (CIKM), pp , [12] K. Vu, K. Hua, H. Cheng, and S. Lang, A Non- Linear Dimensionality-Reduction Technique for Fast Similarity Search in Large Databases, Proc. ACM SIGMOD Int l Conf. Management of Data, pp , [13] K. Chakrabarti and S. Mehrotra, Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces, Proc. Int l Conf. Very Large Databases (VLDB), pp , Sept [14] P. Ciaccia and M. Patella, PAC Nearest Neighbor Queries: Approximate and Controlled Search in High-Dimensional and Metric Spaces, Proc. Int l Conf. Data Eng. (ICDE), pp , [15] H. Jin, B.C. Ooi, H.T. Shen, C. Yu, and A. Zhou, An Adaptive and Efficient Dimensionality Reduction Algorithm for High-Dimensional Indexing, Proc. Int l Conf. Data Eng. (ICDE), pp , Mar [16] R. Weber and K. Bo hm, Trading Quality for Time with Nearest Neighbor Search, Proc. Seventh Int l Conf. Extending Database Technology (EDBT): Advances in Database Technology, pp , [17] E. Tuncel, H. Ferhatosmanoglu, and K. Rose, VQ-Index: An Index Structure for Similarity Searching in Multimedia Databases, Proc. ACM Int l Conf. Multimedia, pp , [18] A. Gionis, P. Indyk, and R. Motwani, Similarity Search in High Dimensions via Hashing, Proc. Int l Conf. Very Large Databases (VLDB), pp , Sept [19] P. Ciaccia and M. Patella, Approximate Similarity Queries: A Survey, Technical Report CSITE-08-01, May ISSN: Page3715

6 [20] E. Tuncel, P. Koulgi, and K. Rose, Rate- Distortion Approach to Databases: Storage and Content-Based Retrieval, IEEE Trans. Information Theory, vol. 50, no. 6, pp , June [21] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis.John Wiley & Sons, [22] A. Gersho and R.M. Gray, Vector Quantization and Signal Compression.Kluwer Academic Publishers, [23] T. Zhang, R. Ramakrishnan, and M. Livny, BIRCH: An Efficient Data Clustering Method for Very Large Databases, Proc. ACM SIGMOD Int l Conf. Management of Data, pp , [24] Sharadh Ramaswamy, Student Member, IEEE, and Kenneth Rose, Fellow, IEEE, Adaptive Cluster Distance Bounding for High-Dimensional Indexing, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 23, NO. 6, JUNE AUTHORS Ashwini is student of DRK Institute of Science and Technology, Hyderabad, AP, INDIA. She has received B.Tech Degree computer science and engineering, M.Tech Degree in computer science and engineering. Her main research interest includes data mining, Databases and DWH. K.Praveen is working as an Associate Professor in DRK Institute of Science and Technology, JNTUH, Hyderabad, Andhra Pradesh, India. He has completed M.Tech (C.S.E) from Osmania University, Hyderabad. His main research interest includes Databases, Web Methods and Computer Networks. Dr.R.V.Krishnaiah (Ph.D) is working as Principal at DRK INSTITUTE OF SCINCE & TECHNOLOGY, Hyderabad, AP, INDIA. He has received M.Tech Degree EIE and CSE. His main research interest includes Data Mining, Software Engineering. ISSN: Page3716

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