Publications

@inproceedings{9157269,

title = {A Self Adaptive FCM Cluster Forests Based Feature Selection},

author = {Ines Lahmar and Aida Zaier and Mohamed Yahia and Ridha Bouallegue},

doi = {10.1109/MMS48040.2019.9157269},

issn = {2157-9830},

year  = {2019},

date = {2019-10-01},

booktitle = {2019 IEEE 19th Mediterranean Microwave Symposium (MMS)},

pages = {1-4},

abstract = {Ensemble clustering refers to combine many clustering methods to produce better results. In this context, we propose a new clustering ensemble method inspired from cluster forests (CF) based Self-Adaptive Fuzzy C-Means (SAFCM) method. Firstly, unsupervised feature selection methodology based on the building of best variables on simulated datasets. Next, we ameliorate the CF algorithm with the integration of SAFCM to find also the best number of K groups. Finally, the modified version normalized cuts spectral clustering (Ncut) is applied to general grouping. The proposed algorithm was tested on datasets from UCI Machine Learning Repository. The experimental results indicate that our proposed method outperforms both different clustering algorithms in terms of clustering quality.},

keywords = {Clustering algorithms;Forestry;Indexes;Feature extraction;Partitioning algorithms;Measurement;Clustering methods;Feature Selection;Clustering;FCM;Density;Spectral clustering;Graph Theory},

pubstate = {published},

tppubtype = {inproceedings}

}