# 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  
ML algorithms are capable of handling a large amount of data, as well as providing features such as frequent pattern mining [77], anomaly detection, predictive modeling, and optimization that can lead to better situational awareness, more efficient, dynamic, and adaptive control [78]. Depending on the characteristics of the specific application and on the requirements of the use case, some algorithms perform better than others [34]. Some ML algorithms have aimed at improving matching time and accuracy, e.g., DL for information retrieval and multimodal interaction [79]. Classification is perhaps the most familiar and most popular DM technique [80]. The goal of classification algorithms is to find functions and models in order to identify to which of a set of categories (sub-populations) a new observation belongs to, based on a training set of data containing observations (or instances) whose category membership is known [58]. Classification is a paradigm of supervised learning. Supervised learning consists of two main phases: (1) the training or construction phase in which the model learns from a given set of labeled samples and (2) the classification phase, which outputs a label with the maximum likelihood for a given sample. It is shown that classifier performance typically increases as the volume of training data grows [81]. Decision Trees (DT) is a widely used technique that has been effective for regression and classification. The DT predictive model represents observations in the branches and conclusions about the target value in the leaves. In the first stage (construction), the DT breaks down a data set into subsets by DTs, and later an associated DT is incrementally developed [77]. In the second stage, pruning is carried out inside the nodes, replacing the node, if necessary, with a regression plane [62]. DT is one of the most popular learning techniques. It can naturally handle data of mixed types and missing values, which occur in most data sets in a smart city application [82]. Some of the methods for DT construction include Classification and Regression Trees (CART), ID3, C4.5 (J48), and T3 [83; 84; 85; 86; 87]. Random Forest (RF) is an ensemble learning model for classification and regression that operates by developing a batch of DT at training time [75]. The output class is the mean prediction (in regression problems) or mode of the classes (in classification) of the individual trees. To make a prediction at a new point, random forests take the majority vote among the outputs of the grown trees in the ensemble. The number of trees in the ensemble is selected through cross-validation [52]. RF runs very efficiently both on scaled and not scaled data [88; 89]. It can handle data sets with unbalanced classes and generates strong predictive models, dealing well with the issue of overfitting. Two features make RF an attractive prediction technique: its ability to achieve high prediction accuracy and its usability of desired capabilities, such as daily electricity data consumed by various appliances. These two characteristics make RF a unique and desired model for analyzing smart city data [10]. Support Vector Machine (SVM) is a prominent supervised learning model that efficiently performs data classification and thereby finds its applications in many real-world scenarios, such as disease diagnosis [77], activity recognition [85], anomaly detection [34], text classification [90], face recognition [74], electricity consumption prediction [91], and finding available parking slots [92]. The SVM training algorithm seeks a separating hyperplane in the feature space that maximizes the distance of each input data point from the hyperplane. The minimum overall distance is called margin [93]. Often, in real-world data, the sample points are not linearly separable. For this reason, the original space is mapped through a kernel function into a higher-dimensional space, where presumably linear separation can be achieved [94].