Abstract This Approach comprises of methods to produce novel and efficient methods to implement search of data objects in various applications. It is based on the best match search to implement proximity or best match search over complex or more than one data source. In particular with the availability of very large numeric data set in the present day scenario. The proposed approach which is based on the Arithmetic measures or distance measures called as the predominant Mean based algorithm. It is implemented on the longest common prefix of data object that shows how it can be used to generate various clusters through combining or grouping of data, as it takes O(log n) computational time. And further the approach is based on the process of measuring the distance which is suitable for a hierarchy tree property for proving the classification is needed one for storing or accessing or retrieving the information as required. The results obtained illustrates overall error detection rates in generating the clusters and searching the key value for Denial of Service (DOS) attack 5.15%, Probe attack 3.87%, U2R attack 8.11% and R2L attack 11.14%. as these error detection rates denotes that our proposed algorithm generates less error rates than existing linkage methods.

References

[1] Adel’son-Vel’skii M. and Landis E., “An Algorithm for the Organization of information,” Accession number: AD0406009, 1962.

[2] Akeem O., Ogunyinka T., and Abimbola B., “A Framework for Multime- Dia Data Mining in Information Technology Environment,” International Journal of Computer Science and Information Security, vol. 10, no. 5, pp. 69-77, 2012.

[3] Andersson A., “Balanced Search Trees Made Simple,” in Proceeding of the Workshop on Algorithms and Data Structures, Montreal, pp. 60-71, 993.

[4] Baeza-Yates R. and Ribeiro-Neto B., Modern Information Retrieval, Wesley Longman Publishing Co, 1999.

[5] Bayer R., “Symmetric Binary B-trees: Data Structure and Maintenance Algorithms,” Acta Informatica, vol. 1, no. 4, pp. 290-306, 1972.

[6] Bentley J., “Multidimensional Binary Search Trees Used for Associative Searching,” Communications of the ACM, vol. 18, no. 9, pp. 509-517, 1975.

[7] Chang H. and Iyengar S., “Efficient Algorithms to Globally Balance a Binary Search Tree,” Communication of ACM, vol. 27, no. 7, pp. 695- 702, 1984.

[8] Day A., “Balancing a Binary Tree,” Computer Journal, vol. 19, no. 4, pp. 360-361, 1976.

[9] Duro D., Franklin S., and Dubé M., “A Comparison of Pixel-based and Object-based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using Spot-5 {HRG} Imagery,” Remote Sensing of Environment, vol. 118, no. 3, pp. 259-272, 2012.

[10] Eamani R., VinodhKumar N., and Jakkamsetti G., “K-Means Clustering Algorithm and Architecture: A Brief Survey,” International Journal of Advanced Science and Technology, vol. 29, no. 06, pp. 2955-2967, 2020.

[11] George A., “Efficient High Dimension Data Clustering using Constraint-Partitioning K- Means Algorithm,” The International Arab Journal of Information Technology, vol. 10, no. 5, pp. 467-476, 2013.

[12] Jing X., Bi Y., and Deng H., “An Innovative Two-Stage Fuzzy KNN-DST Classifier for Unknown Intrusion Detection,” The International Arab Journal of Information Technology, vol. 13, no. 4, pp. 359- 366, 2016.

[13] Khan S. and Khayal M., “A Survey on Maintaining Binary Search Tree in Optimal Shape,” International Conference on Information Management and Engineering, Kuala Lumpur, pp. 365-369, 2009. 720 The International Arab Journal of Information Technology, Vol. 19, No. 5, September 2022

[14] Kononenko I. and Kukar M., Machine Learning and Data Mining, Horwood Publishing, 2007.

[15] Kumar R., Reddy B., and Pappula P., “An Evaluation of Feature Selection Algorithms in Machine Learning,” International Journal of Scientific and Technology Research, vol. 8, no. 12, pp. 2071-2074, 2019.

[16] Martin W. and Ness D., “Optimizing Binary Search Trees Grown with a Sorting Algorithm,” Communication of ACM, vol. 15, no. 2, pp. 88- 93, 1972.

[17] Praveen P. and Rama B., “An Efficient Smart Search Using R Tree on Spatial Data,” Journal of Advanced Research in Dynamical and Control Systems, vol. 4, 2017.

[18] Praveen P. and Rama B., “An Optimized Clustering Method To Create Clusters Efficiently,” Journal of Mechanics of Continua and Mathematical Sciences, vol. 15, no. 1, pp. 339-348, 2020.

[19] Praveen P. and Rama B., “A Novel Approach to Improve the Performance of Divisive Clustering- BST,” Data Engineering and Intelligent Computing, vol. 542, pp. 553-562, 2018.

[20] Praveen P. and Babu J., “Big Data Clustering: Applying Conventional Data Mining Techniques in Big Data Environment,” Innovations in Computer Science and Engineering, pp. 509-516, 2019.

[21] Rama B., Praveen P., Sinha H., and Choudhury T., “A Study on Causal Rule Discovery with PC Algorithm,” in Proceedings of the International Conference on Infocom Technologies and Unmanned Systems, Dubai, pp. 616-621, 2017.

[22] Rauber A., Merkl D., and Dittenbach M., “The Growing Hierarchical Self-organizing Map: Exploratory Analysis of High-dimensional Data,” IEEE Transactions on Neural Networks and Learning Systems, vol. 13, no. 6, pp. 1331-1341, 2002.

[23] Shaik M., “A Survey on Text Classification methods through Machine Learning Methods,” International Journal of Control and Automation, vol. 12, no. 6, pp. 390-396, 2019.

[24] Shaik M., Praveen P., and Prakash R., “Novel Classification Scheme for Multi Agents,” Asian Journal of Computer Science and Technology, vol. 8, no. S3, pp. 54-58, 2019.

[25] Shaik M., “Time Series Forecasting Using Vector Quantization,” International Journal of Advanced Science and Technology, vol. 29, no. 4, pp. 169-175, 2020.

[26] Sheshikala M., Rao D., and Prakash R., “Computation Analysis for Finding Co-Location Patterns using Map-Reduce Framework,” Indian Journal of Science and Technology, vol. 10, no. 8, pp. 12-19, 2017.

[27] Seidel R. and Aragon C., “Randomized Search Trees,” Algorithmica, vol. 16, no. 4, pp. 464-497, 1996.

[28] Silpa-Anan C. and Hartley R., “Optimised KD- Trees for Fast Image Descriptor Matching,” in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, pp. 1-8, 2008.

[29] Sleator D. and Tarjan R., “Self Adjusting Binary Search Trees,” Journal of the Association for Computing Machinery, vol. 32, no. 3, pp. 652- 686, 1985.

[30] Stout Q. and Warren B., “Tree Rebalancing in Optimal time and Space,” Communications of the ACM, vol. 29, no. 9, pp. 902-908, 1986.

[31] Tamilarasi A., Abarna A., Chitra K., Nagendhiran K., and Aarthi R., “Effective Data Clustering Using K Means Along with Lion Optimization Algorithm International,” Journal of Advanced Science and Technology, vol. 29, no. 4, pp. 3835, 2020.

[32] Vinod P. and Maple C., “Maintaining a Binary Search Tree Dynamically,” in Proceedings of the 10th International Conference on Information Visualization, London, pp. 483-488, 2006.

[33] Wang J., Wang N., Jia Y., Li J., Zeng G., Zha H., and Hua S., “Trinary-Projection Trees for Approximate Nearest Neighbor Search,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 2, pp. 388-403, 2014.

[34] Wittek P., Clustering Structure and Quantum Computing. Quantum Machine Learning, Academic Press Elsevier, pp. 99-107. 2014.

[35] Wittek P., Unsupervised Learning. Quantum Machine Learning, Boston: Academic Press, pp. 57-62. 2014.

[36] Yadav S., “An Efficient Affinity Propagation Clustering Technique,” International Journal of Advanced Science and Technology, vol. 29, no. 4, pp. 9555-9573, 2020. Praveen Pappula received his Ph.D. degree in Computer Science from the Kakatiya University (India) in 2019. He is working as Associate Professor in Computer Science and Artificial Intelligence at SR University since 2007. He has published more than 40 referred research papers, 1 book, and has 5 patents filled and pre published. His interests includes Machine Learning, Algorithms Analysis, Data Mining and Programming Languages.