# Data Mining Algorithms for Smart Cities: A Bibliometric Analysis
## 2 Conceptual Framework
### Unsupervised Learning  
Clustering is an unsupervised learning technique aiming at partitioning a collection of data objects into groups (clusters), but unlike classification, the groups are not predefined [80]. The grouping is implemented in a way that intra-cluster dissimilarity is low (according to a set of metrics) and inter-cluster dissimilarity is high [58]. Clustering analysis has been extensively used in problems where there is little prior information available about the data [119], and in a wide range of applications, such as pattern recognition, market research, and image segmentation [120]. In general, the existing clustering algorithms can be broadly categorized into hierarchical clustering, partitioning clustering, model-based clustering, density-based clustering, grid-based clustering, and so on. Hierarchical Clustering (HC) techniques, used by [PERSON] et al. [62], define the cluster distance between two clusters to be the maximum distance between their individual components. At every stage of the clustering process, the two nearest clusters are merged into a new cluster. However, these methods are static, and data objects assigned to a cluster cannot move to a different cluster. Partitioning algorithms are dynamic, and data objects can move from one cluster to another. They can incorporate knowledge about the shape or size of clusters by using appropriate prototypes and distance measures [121]. K-means is a very popular [116] and simple partitioning clustering algorithm [105]. Because of its efficiency, the method is scalable in parallel and distributed systems for big data set handling [49]. K-means assigns \(n\) points to \(k\) clusters using distance as a similarity factor until there is no change to which point belongs to which cluster by iteratively updating cluster centers. However, traditional K-means suffer from sensitivity to the initial selection of cluster centers and difficulty in specifying the number of clusters in advance [121]. [PERSON] et al. in the work of [31] used K-means for clustering two-dimensional GPS position data. In [8], two clustering techniques have been adopted. The first technique was a variance of K-means, Partitioning Around Medoids (PAM), which are the most representative elements in the cluster instead of the centroid as with K-means. The second approach is the model-based Expectation-Maximization algorithm or EM algorithm (EM method). It is a generalization of K-means that uses an iterative process to find the maximum likelihood. [PERSON] et al. in the work of [39] used two very distinct techniques for clustering: K-means and the more advanced Self-Organizing Map (SOM). Although their function in this context is the same (grouping observations based on attribute similarity), the underlying mechanics of both algorithms differ substantially: while K-means tries to optimize an objective function that minimizes cluster variance, SOM employs an iterative approach in which a feedforward NN learns the properties or find unknown relationships among the set of variables that describe a problem, to later assign the original observations to output neurons. The main property of SOM is that it makes a nonlinear projection from a high-dimensional data space on a regular, low-dimensional (usually 2D) grid of neurons. [PERSON] et al. conducted a study for monitoring real traffic and mobility scenarios with the implementation of SOM [35]. [PERSON] et al. [122] adopted another technique for clustering, building a hierarchical tree, represented as a dendrogram. They consider hierarchical clustering as a suitable choice for their study in geographic localization since it does not a priori require the number of clusters. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used in the work of [24; 123]. [PERSON] et al. state that all existing clustering methods have the same difficulty of parameter setting [123].