Enhanced Cluster Ensemble Approach Using Multiple Attributes in Unreliable Categorical Data
DOI:
https://doi.org/10.61841/6149tt83Keywords:
K-Means, Uncertain One Class Classifier, Cluster Ensemble Approach, Support Vector mechanism, Feature Representation., Flexible Flatfoot, Short Foot Exercise, Visual Feedback, Foot Intrinsic Muscles, Foot Alignment.Abstract
Cluster analysis is an efficient tool to identify useful and user-preferable data patterns from categorical data streams. Conventional clustering approaches focused on numerical with single attribute relations from categorical data. Existing approaches perform poorly and have low complexity to combine relative attributes, whether information is present or hidden. Therefore, our proposed Enhanced Categorical Cluster Ensemble Approach (ECCEA) to classify data depends on various different attributes from multidimensional data sources. ECCEA creates a matrix and then converts this matrix into attribute groups with the help of the graph method. Practical outcomes show an effective clustering result with multi-attribute relations with respect to associated attributes from categorical data sets. Further improvement of our proposed approach is to perform well on their corresponding types of attributes to improve the performance with respect to multi-attribute similarity determined for feature-based data exploration using clustering.
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References
[1] Liu, B., Xiao, Y., Philip, S. Y., Cao, L., Zhang, Y., & Hao, Z. (2012). Uncertain one-class learning and
concept summarization learning on uncertain data streams. IEEE Transactions on Knowledge and Data
Engineering, 26(2), 468-484.
[2] Aggarwal, C. C., Xie, Y., & Yu, P. S. (2011, August). On dynamic data-driven selection of sensor streams.
In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data
mining, 1226-1234.
[3] Aggarwal, C.C., & Philip, S.Y. (2008). A survey of uncertain data algorithms and applications. IEEE
Transactions on Knowledge and Data Engineering, 21(5), 609-623.
[4] Bonchi, F., Van Leeuwen, M., & Ukkonen, A. (2011, April). Characterizing uncertain data using
compression. In proceedings of the 2011 SIAM international conference on data mining (pp. 534-545).
Society for Industrial and Applied Mathematics.
[5] Bovolo, F., Camps-Valls, G., & Bruzzone, L. (2010). A support vector domain method for change detection in
multitemporal images. Pattern Recognition Letters, 31(10), 1148-1154.
[6] Chen, L. & Wang, C. (2010). Continuous subgraph pattern search over certain and uncertain graph
streams. IEEE Transactions on Knowledge and Data Engineering, 22(8), 1093-1109.
[7] Liu, B., Xiao, Y., Cao, L., & Philip, S. Y. (2010, December). Vote-based LELC for positive and unlabeled
textual data streams. In 2010 IEEE International Conference on Data Mining Workshops (pp. 951-958).
IEEE.
[8] Murthy, R., Ikeda, R., & Widom, J. (2010). Making aggregation work in uncertain and probabilistic
databases. IEEE Transactions on knowledge and data engineering, 23(8), 1261-1273.
[9] Sun, L., Cheng, R., Cheung, D.W., & Cheng, J. (2010). Mining uncertain data with probabilistic guarantees.
In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data
mining, 273-282.
[10] Takruri, M., Rajasegarar, S., Challa, S., Leckie, C., & Palaniswami, M. (2011). Spatio-temporal
modelling-based drift-aware wireless sensor networks. IET wireless sensor systems, 1(2), 110-122.
[11] Tsang, S., Kao, B., Yip, K.Y., Ho, W.S., & Lee, S.D. (2009). Decision trees for uncertain data. IEEE
transactions on knowledge and data engineering, 23(1), 64-78.
[12] Le, T., Tran, D., Nguyen, P., Ma, W., & Sharma, D. (2011). Multiple distribution data description learning
method for novelty detection. International Joint Conference on Neural Networks, 2321-2326.
[13] Yuen, S.M., Tao, Y., Xiao, X., Pei, J., & Zhang, D. (2009). Superseding nearest neighbor search on uncertain
spatial databases. IEEE Transactions on Knowledge and Data Engineering, 22(7), 1041-1055.
[14] Zhu, X., Ding, W., Philip, S.Y., & Zhang, C. (2011). One-class learning and concept summarization for data
streams. Knowledge and Information Systems, 28(3), 523-553.
[15] Zou, Z., Gao, H., & Li, J. (2010). Discovering frequent subgraphs over uncertain graph databases under
probabilistic semantics. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge
discovery and data mining, 633-642.
[16] Iam-On, N., Boongeon, T., Garrett, S., & Price, C. (2010). A link-based cluster ensemble approach for
categorical data clustering. IEEE Transactions on Knowledge and Data Engineering, 24(3), 413-425.
[17] Boongoen, T., Shen, Q., & Price, C. (2010). Disclosing false identity through hybrid link analysis. Artificial
Intelligence and Law, 18(1), 77-102.
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