Efficient mining of frequent itemsets in social network data based on MapReduce framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social Networks promote information sharing between people everywhere and at all times. Mining data produced in this data-rich environment can be extremely useful. Frequent itemset mining plays an important role in mining associations, correlations, sequential patterns, causality, episodes, multidimensional patterns, max-patterns, partial periodicity, emerging patterns, and many other significant data mining tasks in social networks. With the exponential growth of social network data towards a terabyte or more, most of the traditional frequent itemset mining algorithms become ineffective due to either huge resource requirements or large communications overhead. Cloud computing has proved that processing very large datasets over commodity clusters can be done by providing the right programming model. As a parallel programming model, MapReduce, one of most important techniques for cloud computing, has emerged in the mining of datasets of terabyte scale or larger on clusters of computers. In this paper, we propose an efficient frequent itemset mining algorithm, called IMRApriori, based on MapReduce framework which deals with Hadoop cloud, a parallel store and computing platform. The paper demonstrates experimental results to corroborate the theoretical claims.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it