Distributed Weighted Fuzzy C-Means Clustering for Wireless Sensor Network Data Analysis
Bibliographic record
Abstract
Fuzzy C-means (FCM) is a widely known clustering algorithm, which performs segregation of a dataset by giving equal weightage to all the features associated with it. In case of unbalanced datasets the FCM at times struggles due to this equal feature weighting and initialization sensitivity. Hashemzadeh et. al. in 2019 reported a new FCM method based on feature weight and cluster-weight learning. This paper suitably modify the algorithm of Hashemzadeh et. al. for effective analysis of clusters in distributed processing environment of a wireless sensor network. This proposed algorithm is termed as distributed weighted Fuzzy C-means (DWFCM) clustering. Here DWFCM uses average Euclidean Deviation as the cost function due to the use of clean datasets without outlier and noise, over non-Euclidean distance used by Hashemzadeh et. al. In the simulation DWFCM performance is tested on datasets distributed over six wireless sensor nodes which mutually process the information with diffusion based learning. Performance evaluation is carried out using Silhouette Index (SI) as validation parameter for three data sets : Synthetic dataset Circle_3_2, Thames river water quality dataset, Canada Weather station dataset. Simulation results reveal superior performance of proposed DWFCM over distributed FCM algorithm using visual clusters obtained at each nodes, SI value plot at the sensor nodes, average convergence plot.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".