Twitter dataThe tremendous growth in the technology has led to the accumulation of enormous Big Data. Techniques that efficiently analyse this Big Data are in great demand. Tweets from Social media and Sensor data are some of the most common forms of Big Data. Machine learning algorithms pave ...
In information retrieval and machine learning, a good number of techniques utilize the similarity/distance measures to perform many different tasks [1]. Clustering and classification are the most widely-used techniques for the task of knowledge discovery within the scientific fields [2,3,4,5,6,7,...
Big dataFragmented periodogramSpectral clusteringSmoothed periodogramTime series clusteringWe propose and study a new frequency-domain procedure for characterizing and comparing large sets of long time series. Instead of using all the information available from data, which would be computationally very ...
Data-driven classification of disease is a recent idea, made possible by access to large population studies, such as UK Biobank5. Examples include using molecular or imaging data to identify and classify subtypes of disease such as metabolic syndrome6, amyotrophic lateral sclerosis (ALS)7, cancer8...
Current and future applications of statistical machine learning algorithms for agricultural machine vision systems 2.2.1K-Means Clustering TheK-means clusteringis an unsupervised learning technique that used unlabelled data for classification. The principle of this classifier is to find groups in the data...
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This paper presents a comprehensive survey of the state-of-the-art data stream mining algorithms with a focus on clustering and classification because of their ubiquitous usage. It identifies mining constraints, proposes a general model for data stream mining, and depicts the relationship between ...
Article Open access 23 September 2020 Band-based similarity indices for gene expression classification and clustering Article Open access 03 November 2021 A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data Article Open access 28 August 2020 Intr...
The tasks of data mining include association analysis, cluster analysis, classification analysis, anomaly analysis, specific group analysis, and evolution analysis [21]. One of the most common uses of Hadoop is web search. While it is not the only software framework application, it stands out as...
You might find these chapters and articles relevant to this topic. Chapter Visual Data-Mining Techniques 43.5 Clustering Clustering is the process of finding a partitioning of the dataset into homogeneous subsets called clusters. Unlike classification, clustering is unsupervised learning. This means ...