@article{Qaiyum2020-nu,

title = {Incremental Interval Type-2 fuzzy clustering of data Streams  using single pass method},

author = {Sana Qaiyum and Izzatdin Abdul Aziz and Mohd Hilmi Hasan and Asif Irshad Khan and Abdulmohsen Almalawi},

year  = {2020},

date = {2020-06-01},

urldate = {2020-06-01},

journal = {Sensors (Basel)},

volume = {20},

number = {11},

pages = {3210},

publisher = {MDPI AG},

abstract = {Data Streams create new challenges for fuzzy clustering 

 algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). 

 One problem associated with IT2FCM is that it tends to be 

 sensitive to initialization conditions and therefore, fails to 

 return global optima. This problem has been addressed by 

 optimizing IT2FCM using Ant Colony Optimization approach. 

 However, IT2FCM-ACO obtain clusters for the whole dataset which 

 is not suitable for clustering large streaming datasets that may 

 be coming continuously and evolves with time. Thus, the clusters 

 generated will also evolve with time. Additionally, the incoming 

 data may not be available in memory all at once because of its 

 size. Therefore, to encounter the challenges of a large data 

 stream environment we propose improvising IT2FCM-ACO to generate 

 clusters incrementally. The proposed algorithm produces clusters 

 by determining appropriate cluster centers on a certain 

 percentage of available datasets and then the obtained cluster 

 centroids are combined with new incoming data points to generate 

 another set of cluster centers. The process continues until all 

 the data are scanned. The previous data points are released from 

 memory which reduces time and space complexity. Thus, the 

 proposed incremental method produces data partitions comparable 

 to IT2FCM-ACO. The performance of the proposed method is 

 evaluated on large real-life datasets. The results obtained from 

 several fuzzy cluster validity index measures show the enhanced 

 performance of the proposed method over other clustering 

 algorithms. The proposed algorithm also improves upon the run 

 time and produces excellent speed-ups for all datasets.},

keywords = {änt colony optimization; data stream; incremental learning;   interval type-2 fuzzy c-means"},

pubstate = {published},

tppubtype = {article}

}