Cooperative Partitional-Divisive Clustering and Its Application in Gene Expression Analysis
Bibliographic record
Abstract
Clustering techniques organize a collection of objects into cohesive groups called clusters such that objects in the same cluster are more similar to each other than objects in different clusters. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Combining multiple clustering is an approach to overcome the deficiency of single algorithms and further enhance their performances. Current approaches to combining multiple clusterings use end-result cooperation (e.g. ensemble clustering and hybrid clustering) between the clustering algorithms. Inherent drawbacks of the end-result cooperation are: the computational complexity of ensemble clustering and the idle wasted time in the hybrid approaches. In this paper, the k-means and the bisecting k-means clustering algorithms are both combined using intermediate-cooperation strategy for the aim of obtaining better clustering solutions than non-cooperative algorithms. Undertaken experimental results show that the quality of the clustering solutions obtained from the cooperative partitional-divisive clustering (CPDC) model is better than those obtained from the non-cooperative algorithms over a number of gene expression datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".