Publication Type : Conference Paper
Thematic Areas : Learning-Technologies
Publisher : 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).
Source : 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), 2016.
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980332299&doi=10.1109%2fICCTICT.2016.7514571&partnerID=40&md5=a1e8589765be706bf89c528fe3792ca7
ISBN : 9781509000821
Keywords : Algorithm design and analysis, Apriori, association rule generation, Classification algorithms, Clustering algorithms, Data mining, Feature space, functional genomics classification, Information Retrieval, K-means clustering, label space, mixed data, Multi label classification, multi label prediction, multilabel classification, music categorization, numerical data, Prediction algorithms, rule mining, rule mining algorithm apriori, semantic scene classification, simple k-means, Testing, Training
Campus : Amritapuri
School : School of Engineering
Center : Technologies & Education (AmritaCREATE), Amrita Center For Research in Analytics
Department : Computer Science
Year : 2016
Abstract : Lately, modern applications like information retrieval, semantic scene classification, music categorization and functional genomics classification highly require multi label classification. A rule mining algorithm apriori is widely used for rule generation. But Apriori is used many times on categorical data, it is seldom used for numerical data. This leads to an idea that with proper data pre-processing, a lot of intangible rules can be derived from such numerical datasets. Since the algorithm will check each and every datasets, we used a simple k-means clustering approach for dividing the processing space of Apriori and thus rules are generated for each cluster. The accuracy of the algorithm is calculated using hamming loss and is presented in the paper. This hybrid algorithm directly aims to find out hidden patterns in huge numerical datasets and make reliable label prediction easier.
Cite this Research Publication : H. Haripriya, Prathibhamol CP, Pai, Y. R., Sandeep, M. S., Sankar, A. M., a, S. N. V., and Prof. Prema Nedungadi, “Multi label prediction using association rule generation and simple k-means”, in 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), 2016.