# Data Mining Algorithms for Smart Cities: A Bibliometric Analysis
## 2 Conceptual Framework
### Data Mining Technologies for Smart Cities
#### 2.4.2 Machine Learning (ML) Algorithms
Furthermore, we prefer a hyperplane that better separates much of the data, even if it ignores a few misclassified samples [52]. To bridge the gap between ideal assumptions and realistic constraints, [PERSON] et al. [81] proposed the secure SVM, which is a privacy-preserving SVM training scheme over blockchain-based encrypted IoT data. When the classification is based on more than two classes, we can use multi-class SVM, which classifies the output into more classes. Initially, SVM was mainly proposed for binary classification, but later it tuned into multi-class classification due to a variety of methods. Two methods are mostly used for multi-classification problems: (1) by reducing it to multiple binary classification problems [47] and (2) one versus one [95]. [PERSON] et al. in the work of [34] used One-Class SVM (OC-SVM), which is a special case of semi-supervised SVMs that do not require labeled data. OC-SVMs build a frontier to classify new samples as normal or outlier. A Bayesian Network (BN) is a concise representation of the direct dependencies between a set of statistical variables formed in a directed acyclic graph and a set of node probability tables [10]. In data modeling, a BN creates implicit assumptions about dependencies between variables, though, in the real world, two variables are theoretically never truly and fully independent. A Naive Bayes (NB) classifier is technically a special case of BN, using [PERSON] theorem in a naive way since it assumes every predictor variable is conditionally independent on the class (i.e., attribute) label [96]. Hence, an NB is a simple stochastic classifier based on applying [PERSON] theorem with strong independence assumptions [10]. [PERSON] et al. in the work of [93] deployed the Likelihood Ratio Test (LRT), which is an NB classifier and assumes that individual features (elements) of a feature vector \(\mathrm{x}=(\mathrm{x}_{1},\ldots,\mathrm{x}_{n})\) are independent random variables. K Nearest Neighbors (KNN) is a simple regression model that estimates the output of new samples based on the average output of its k nearest neighbors. Nearest neighbors are found in the feature space [76]. To make a prediction, this technique aggregates the values of the K \"closest\" examples in the training set, where K is an input parameter [97]. To compute the distance among observations, we can use the Euclidean or Manhattan distance measures. KNN is a type of instance-based learning, or lazy learning, in which the function is only approached locally, and all calculations are postponed until classification [62]. The Artificial Neural Network (ANN) is a very popular technique, which relies on supervised learning [92; 98]. An ANN is a strong, nonlinear modeling tool that imitates the operation of biological neurons. Training an ANN involves the tuning of the weights and biases of the network. The objective is to maximize the network prediction performance, which corresponds to minimizing the difference between all network outputs \(\mathrm{y}_{\mathrm{k}}\) and desired outputs or targets \(\mathrm{t}_{\mathrm{k}}\) on validation data [94]. Using the Back Propagation (BP) algorithm [99], the NN can learn relevant statistical information from a suitable amount of training data, and the mathematical information learned can reflect the function mappingrelation of the input-output data model [100]. An earlier ANN architecture is Multilayer Perceptron (MLP), a Neural Network (NN) with a fully connected architecture. Generally, MLP performs well and has been applied widely. A quicker-to-train but more memory-intensive network is the Radial Basis Function (RBF) network [101], used by [PERSON] et al. in the work of [84] for building a regression model that considers many variables. At present, many new architectures have been developed for ANN [102]. ANNs tend to overfit, which means to be trained to fit the noise trend, but without producing a suitable generalization, as expected by ANN.