Towards a New Approach for Empowering the MR-DBSCAN Clustering for Massive Data Using Quadtree
Bibliographic record
Abstract
Multiple emerging technologies like social networks and IoT generate huge amounts of data on daily basis. This leads us to analyze and cluster this data, so we can uncover hidden values and patterns. DBSCAN is a powerful clustering algorithm which detects patterns by clustering data based on its density, it classifies each point as a core point, border point or a noise. DBSCAN is already used in many applications like retail business, medical imaging and text mining. However, the existence of advanced networks and sophisticated machines increased the need to switch traditional clustering algorithms from single node to parallel nodes environment. In our paper, we present a solution to parallelize DBSCAN by using Quadtree data structure. Our solution distributes the dataset into smaller chunks, then it utilizes the parallel programming frameworks such as Map-Reduce to provide an infrastructure to store and process these small chunks of data. We use various training sets to evaluate the performance of both traditional DBSCAN and our Map-Reduce DBSCAN prototype. We analyze our solution in terms of time complexity, efficiency, scalability, value and accuracy. Our analysis illustrates the benefits of using parallelized DBSCAN clustering, it shows the usefulness of managing subsets of data using Quadtree data structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".