Balanced-partitioning treemapping method for digital hierarchical dataset
Bibliographic record
Abstract
The problem of visualizing a hierarchical dataset is an important and useful technical in many real happened situations. Folder system, stock market, and other hierarchical related dataset can use this technical for better understanding the structure, dynamic variation of the dataset. Traditional space-filling(square) based methods have advantages of compact space usage, node size showing compared to diagram based methods. While space-filling based methods have two main research directions—static and dynamic performance. We present a treemapping method based on balanced partitioning that enables in one variant very good aspect ratios, in another good temporal coherence for dynamic data and in the third a good compromise between these two aspects. To layout a treemap, we divide all children of a node into two groups. These groups are further divided until we reach groups of single elements. Then these groups are combined to form the rectangle representing the parent node. This process is performed for each layer of a given hierarchical dataset. In one variant of our partitioning we sort child elements first and built two as equal as possible sized groups from big and small elements(size-balanced partition), which achieves good aspect ratios for the rectangles, but less good temporal coherence(dynamic). The second variant takes the sequence of children and creates the as equal as possible groups with-out sorting(sequence-based, good compromise between aspect ratio and temporal coherency). The third variant splits the children sets always into two groups of equal cardinality regardless of their size(number-balanced, worse aspect ratios but good temporal coherence). We evaluate aspect ratios and dynamic stability of our methods and propose a new metric that measures the visual difference between rectangles during their movement for representing temporally changing inputs. We demonstrate that our treemapping via balanced partitioning out performs state-of-the-art methods for a number of real-world datasets.
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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 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".