Scalable clustering of categorical data and applications
Bibliographic record
Abstract
Clustering is widely used to explore and understand large collections of data. In this thesis, we introduce LIMBO, a scalable hierarchical categorical clustering algorithm based on the Information Bottleneck (IB) framework for quantifying the relevant information preserved when clustering. As a hierarchical algorithm, LIMBO can produce clusterings of different sizes in a single execution. We also define a distance measure for categorical tuples and values of a specific attribute. Within this framework, we define a heuristic for discovering candidate values for the number of meaningful clusters. Next, we consider the problem of database design, which has been characterized as a process of arriving at a design that minimizes redundancy. Redundancy is measured with respect to a prescribed model for the data (a set of constraints). We consider the problem of doing database redesign when the prescribed model is unknown or incomplete. Specifically, we consider the problem of finding structural clues in a data instance, which may contain errors, missing values, and duplicate records. We propose a set of tools based on LIMBO for finding structural summaries that are useful in characterizing the information content of the data. We study the use of these summaries in ranking functional dependencies based on their data redundancy. We also consider a different application of LIMBO, that of clustering software artifacts. The majority of previous algorithms for this problem utilize structural information in order to decompose large software systems. Other approaches using non-structural information, such as file names or ownership information, have also demonstrated merit. We present an approach that combines structural and non-structural information in an integrated fashion. We apply LIMBO to two large software systems, and the results indicate that this approach produces valid and useful clusterings. Finally, we present a set of weighting schemes that specify objective assignments of importance to the values of a data set. We use well established weighting schemes from information retrieval, web search and data clustering to assess the importance of whole attributes and individual values.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".