Semi-supervised Clustering Algorithm for Retention Time Alignment of Gas Chromatographic Data
نویسندگان
چکیده
Gas chromatography (GC) is an effective tool for the analysis of complex mixtures with a huge number components. To keep tracking chemical changes during processes like plastic waste pyrolysis usually different sample states are profiled, but retention time drifts between chromatograms make comparability difficult. The aim this study to develop fast and simple method eliminate using easily accessible priori information. proposed tested on GC obtained by product (Mg/Y catalyst) shredded real HDPE/PP/LDPE mixture. A modified k-means algorithm was developed account samples (different states). outcome alignment averaged each peak from all which makes comparison further (such as "fingerprinting") easier or possible.
منابع مشابه
Tri-training and Data Editing Based Semi-supervised Clustering Algorithm
Semi-Supervised clustering algorithms often utilize a seeds set consisting of a small amount of labeled data to initialize cluster centroids, hence improve the clustering performance over whole data set. Both the scale and quality of seeds set directly restrict the performance of semi-supervised clustering algorithm. In this paper, a new algorithm named DE-Tri-training semi-supervised K-means i...
متن کاملAn Effective Semi-supervised Divisive Clustering Algorithm
Nowadays, data are generated massively and rapidly from scientific fields such as bioinformatics, neuroscience and astronomy to business and engineering fields. Cluster analysis, as one of the major data analysis tools, is therefore more significant than ever. Here, we propose an effective Semi-supervised Divisive Clustering algorithm (SDC). Data points are first organized by a minimal spanning...
متن کاملAn Improved Semi-supervised Fuzzy Clustering Algorithm
Semi-supervised clustering is an important method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering based on Mahalanobis distance and Gaussian Kernel for SCAPC algorithm. Here, we give a new semi-supervised fuzzy clustering objective function. By solving the optimization problem with above objec...
متن کاملSemi-supervised incremental clustering of categorical data
Résumé. Le clustering semi-supervisé combine l’apprentissage supervisé and non-supervisé pour produire meilleurs clusterings. Dans la phase initiale supervisée de l’algorithme, un échantillon d’apprentissage est produit par selection aléatoire. On suppose que les exemples de l’échantillon d’apprentissage sont étiquetés par un attribut de classe. Puis, un algorithme incrémentiel développé pour l...
متن کاملExtracting Prior Knowledge from Data Distribution to Migrate from Blind to Semi-Supervised Clustering
Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Periodica Polytechnica Chemical Engineering
سال: 2022
ISSN: ['1587-3765', '0324-5853']
DOI: https://doi.org/10.3311/ppch.18834