| 1. |
- Fuchs, Matthias, 1970-, et al.
(author)
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Clustering : Hierarchical, k-Means, DBSCAN
- 2022
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In: Applied Data Science in Tourism. - Cham : Springer Nature. - 9783030883881 - 9783030883898 ; , s. 129-149
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Book chapter (peer-reviewed)abstract
- This chapter will discuss the unsupervised machine learning technique known as clustering and its main approaches and use cases. After presenting typical application areas for the tourism industry, the mathematical principle of clustering will be explained. Various techniques for representing differences between cases or clusters will be introduced, and major methods used to form clusters based on these differences will be presented (i.e., single linkage, complete linkage, average linkage, and centroid). Subsequently, the three most widely applied clustering approaches will be described. First, major concepts of hierarchical clustering, like divisive and agglomerative techniques, will be highlighted. Second, the partitioning technique k-means will be introduced, and, third, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) will be discussed. By using real tourism data and the data science platform RapidMiner, the practical demonstration will then explain step-by-step how clustering approaches can be executed. After employing typical processes for data transformation and normalization, RapidMiner processes for k-means, hierarchical clustering, and DBSCAN will be shown, and the clustering results will be discussed. Lastly, a tourism case applying k-means and DBSCAN to identify points of interest based on uploaded photo data extracted from the platform Flickr will conclude the chapter.
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| 2. |
- Oskolkov, Nikolay, et al.
(author)
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Dimensionality Reduction : Overview, Technical Details, and Some Applications
- 2022
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In: Applied data science in tourism : Interdisciplinary approaches, methodologies, and applications - Interdisciplinary approaches, methodologies, and applications. - Cham : Springer International Publishing. - 2366-262X .- 2366-2611. - 9783030883898 - 9783030883881 ; , s. 151-167
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Book chapter (peer-reviewed)abstract
- Dimensionality reduction is an Exploratory Data Analysis (EDA) approach allowing for fast visualization of high-dimensional data and the possibility of discovering hidden systematic patterns within a data set. While linear dimensionality reduction techniques, such as Principal Component Analysis (PCA), are considered the golden standard in many areas of data science, they seem to be inadequate for analyzing non-linear high-dimensional data (e.g., images, text, gene expression). Instead, in this case, non-linear dimensionality reduction with t-distributed Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used, providing state-of-the-art methods to explore high-dimensional data. This chapter will give an overview of dimension reduction techniques, with a particular focus on PCA, tSNE, and UMAP and their applications within the fields of data science and computational biology.
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