1
|
Najib FM, Ismail RM, Badr NL, Gharib TF. Incomplete high dimensional data streams clustering. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-200297] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Many recent applications such as sensor networks generate continuous and time varying data streams that are often gathered from multiple data sources with some incompleteness and high dimensionality. Clustering such incomplete high dimensional streaming data faces four constraints which are 1) data incompleteness, 2) high dimensionality of data, 3) data distribution, 4) data streams’ continuous nature. Thus, in this paper, we propose the Subspace clustering for Incomplete High dimensional Data streams (SIHD) framework that overcomes the above clustering issues. The proposed SIHD provides continuous missing values imputation for incomplete streams based on the corresponding nearest-neighbors’ intervals. An adaptive subspace clustering mechanism is proposed to deal with such incomplete high dimensional data streams. Our experimental results using two different data sets prove the efficiency of the proposed SIHD framework in clustering such incomplete high dimensional data streams in terms of accuracy, precision, sensitivity, specificity, and F-score compared to five algorithms GFCM, GBDC-P2P, DS, Ensemble, and DMSC. The proposed SIHD improved: 1) the accuracy on average over the five algorithms in the same mentioned order by 11.3%, 10.8%, 6.5%, 4.1%, and 3.6%, 2) the precision by 15%, 10.6%, 6.4%, 4%, and 3.5%, 3) the sensitivity by 16.6%, 10.6%, 5.8%, 4.2%, and 3.6%, 4) the specificity by 16.8%, 10.9%, 6.5%, 4%, and 3.5%, 5) the F-score by 16.6%, 10.7%, 6.6%, 4.1%, and 3.6%.
Collapse
Affiliation(s)
- Fatma M. Najib
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Rasha M. Ismail
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Nagwa L. Badr
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Tarek F. Gharib
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| |
Collapse
|
2
|
Najib FM, Ismail RM, Badr NL, Gharib TF. Clustering based approach for incomplete data streams processing. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Fatma M. Najib
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Rasha M. Ismail
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Nagwa L. Badr
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Tarek F. Gharib
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| |
Collapse
|