Knowledge Expansion Algorithm of Heterogeneous Network Big Data Based on Improved K-means Algorithm
Bibliographic record
Abstract
In recent years, with the rapid progress of wireless communication technology and various intelligent terminal technologies, all kinds of business requirements have shown explosive growth. The high quality of service requirements of diversified services and large-scale network capacity problems have become major challenges that wireless networks will face. In order to meet the business needs of different users, rational NP is the most effective and economic method to improve the system capacity. However, how to achieve higher network throughput at a lower cost is a very important research topic. The main purpose of this paper is to study the knowledge expansion algorithm of heterogeneous network (HN) big data based on the improved K-means algorithm (IKA). This paper will focus on wireless network technology, NP and other related content. In addition, this paper will describe the relevant theories of big data technology for NP. This paper proposes a BS clustering scheme that can be applied to ultra-dense network scenarios. By using the proposed clustering algorithm, small cell BSIUDN can be effectively clustered, which greatly simplifies the network topology and facilitates the management of BS. At the same time, orthogonal time-frequency resource blocks are allocated within the cluster to reduce system interference to a certain extent. The simulation results show that the proposed KCA based on the improved WD can effectively cluster the small cell BS in the ultra-dense network.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".