Using Markov Models to Mine Temporal and Spatial Data
Bibliographic record
Abstract
The mining of temporal and / or spatial signals by graphical models can have several purposes: Segmentation : in this task, the GM clusters the signal into stationary (or homogeneous) and transient segments or areas (Jain et al., 1999).The term stationnary means that the signal values are considered as independent outcomes of probability density functions (pdf).These areas are then post-processed to extract some valuable knowledge from the data.Pattern matching : in this task, the GM measures the a posteriori probability P(model = someLabel/observedData).When there are as many GM as labels, the best probability allows the classification of an unknown pattern by the label associated with the highest probability.Background modelling : in order to make proper use of quantitative data, the GM is used as a background model to simulate an averaged process behavior that corrects for chance variation in the frequency counts (Huang et al., 2004).The domain expert compares the simulated and real data frequencies in order to distinguish if he / she is facing to overor under-represented data that must be investigated more carefully.In this chapter, we will present a general methodology to mine different kinds of temporal and spatial signals having contrasting properties: continuous or discrete with few or many modalities.This methodology is based on a high order Markov modelling as implemented in a free software: CARROTAGE (see section 3).Section 2 gives the theoretical basis of the modelling.Section 3 describes a general flowchart for mining temporal and spatial signals using CARROTAGE.The next section is devoted to the description of three data mining applications following the same flowchart.Finally, we draw some conclusions in section 5.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".