Microarray Breast Cancer Data Clustering Using Map Reduce Based K-Means Algorithm
Bibliographic record
Abstract
Breast cancer is one of the world's most advanced and most common cancers occurring in women. An early diagnosis of breast cancer offers treatment for it; therefore, several experiments are in development establishing approaches for the early detection of breast cancer. The great increase in research in the last decade in microarray data processing is a potent tool of diagnosing diseases. Based on genomic knowledge, micro-arrays have changed the way clinical pathology recognizes, identifies, and classifies the diseases of humans, particularly those of cancer. In this article, we examined microarray data for breast cancer with the k-means clustering algorithm, but it was hard to scale and process a large number of micro-array data alone. To this end, we use a chart to minimize the paradigm for evaluating microarray data on breast cancer. Moreover, the efficiency of the parallel k-means model is measured with the operating period, the scaling, and all runtime of the model.
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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 | 0.001 | 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".