Mining Profitable and Concise Patterns in Large‐Scale Internet of Things Environments
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
In recent years, HUIM (or a.k.a. high‐utility itemset mining) can be seen as investigated in an extensive manner and studied in many applications especially in basket‐market analysis and its relevant applications. Since current basket‐market scenario also involves IoT equipment to collect information, i.e., sensor or smart devices, it is necessary to consider the mining of HUIs (or a.k.a. high‐utility itemsets) in a large‐scale database especially with IoT situations. First, a GA‐based MapReduce model is presented in this work known as GMR‐Miner for mining closed patterns with high utilization in large‐scale databases. The k ‐means model is initially adopted to group transactions regarding their relevant correlation based on the frequency factor. A genetic algorithm (GA) is utilized in the developed MapReduce framework that can be used to explore the potential and possible candidates in a limited time. Also, the developed 3‐tier MapReduce model can be easily deployed in Spark for the handlings of any database of large scale for knowledge discovery of closed patterns with high utilization. We created sets of extensive experimental environments for evaluating the results of the developed GMR‐Miner compared to the well‐known and state‐of‐the‐art CLS‐Miner. We present our in‐depth results to show that the developed GMR‐Miner outperforms CLS‐Miner in many criteria, i.e., memory usage, scalability, and runtime.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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