首页|Universiti Kebangsaan Malaysia Researcher Details Findings in Ma- chine Learning (Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algo- rithm)
Universiti Kebangsaan Malaysia Researcher Details Findings in Ma- chine Learning (Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algo- rithm)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in artificial intelligence. According to news reporting from Selangor, Malaysia, by NewsRx journalists, research stated, “Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout’s distributed machine-learning environment.” Our news correspondents obtained a quote from the research from Universiti Kebangsaan Malaysia: “The study taps into Apache Hadoop’s robust tools for data storage and processing, including HDFS and MapReduce, to effectively manage and analyze big data challenges. The preprocessing phase utilizes Canopy clustering to expedite the initial partitioning of data points, which are subsequently refined by K- means to enhance clustering performance. Experimental results confirm that incorporating the Canopy as an initial step markedly reduces the computational effort to process the vast quantity of parallel power load abnormalities. The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined.”
Universiti Kebangsaan MalaysiaSelangorMalaysiaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
