Design and Application of a Text Clustering Algorithm Based on Parallelized K-Means Clustering
Bibliographic record
Abstract
The traditional text clustering algorithms face two common problems: the high dimensionality of computing vectors and poor calculation efficiency. To solve these problems, this paper explores deep into the K-means clustering (KMC), Hadoop and Spark big data technique, and then proposes a novel text clustering algorithm based on the KMC parallelized on big data platform. The propose algorithm is denoted as the SWCK-means. First, the Word2vec was adopted to calculate the weights of word vectors, and thus reduce the dimensionality of the massive text data. Next, the Canopy algorithm was introduced to cluster the weight data, and identify the initial cluster centers for the KMC. On this basis, the KMC was employed to cluster the preprocessed data. To improve the efficiency, a parallel design for the Canopy algorithm and the KMC was developed under the Spark architecture. The proposed algorithm was verified through experiments on a massive amount of online text data. The results show that our algorithm achieved more accurate classification effects than the traditional KMC, especially in handling a huge amount of data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".