# Data Mining Algorithms for Smart Cities: A Bibliometric Analysis
## 1 Introduction  
The last few decades have observed an unprecedented trend of people moving to live in urban areas, as cities accelerate time by compressing space [1]. The year 2008 was a milestone when for the first time, the urban population surpassed that of rural areas; it is foreseen that until 2050, two-thirds of the global population will be metropolitan inhabitants [2, 3]. This trend of people moving to cities causes immense pressure on city infrastructures [4]. Ever since the first cities were built, they have been dependent on technology to sustain life and produce ever more sophisticated technologies and tools [5]. Cities are like dynamic living organisms, and they constantly evolve [6]. The smart city is an innovation of the physical city with high integration of advanced monitoring, sensing, communication, and control technologies, aiming to provide real-time, interactive, and intelligent services to citizens [7]. In order to improve the city services, it is very important to infer patterns and analyze citizen behavior [8] because a smart city must focus on the needs of its inhabitants [9]. A city is a complex system to operate, and new methods are required to manage it and use the massive amounts of data it generates [10]. City administrations can gain knowledge that is hidden in large-scale data to provide better urban governance and management by applying Information and Communication Technologies (ICT) solutions. Such ICT solutions enable better transport planning, efficient water management, new energy efficiency strategies, improved waste management, and effective risk management policies for the city users. Moreover, other important aspects of urban life, such as public health, air quality, and pollution, and public security, can also benefit from these ICT solutions [11, 12, 13, 14, 15]. With the fast development of ICT and ubiquitous mobile computing, large quantities of digital traces that register individual human activities at both spatial and temporal axes have becomeavailable [16]. Smart cities administrators can collect information from many sources. These include ambient and mobile device sensor data, as well as Social Media (SM) data. Data can be tapped from city-wide sites, such as power grid status, transportation grid status, vehicular networks, locations of emergency service providers, and the size of crowds in locations throughout the region [17]. The acquired data are highly noisy and redundant, and systematic use of Data Mining (DM) and Machine Learning (ML) techniques can facilitate processing by extracting only relevant information [18]. Compared to traditional processing methods, ML techniques have some distinct advantages in the extraction and release of big data services. Moreover, with advanced manipulations, Deep Learning (DL) and Reinforcement Learning (RL) techniques could achieve high data rate and precision [7]. This study is a review of work associated with DM techniques used in the smart cities' context by using bibliometric analysis. We present a comprehensive overview of the interdisciplinary smart cities research field. This review aims to answer the following research questions:  
1. Which are the main DM techniques used in the context of smart cities? 2. How can the knowledge base for the interdisciplinary field of smart cities and their intellectual structure be identified? 3. How does the field of DM for smart cities quantitatively evolve over time, specifically with regards to publication and citation counts? 4. What is the conceptual structure of data technologies for smart cities? 5. What is the social network structure of data technologies for the smart cities scientific community? The remainder of the article is structured as follows: Section 2 presents a conceptual framework within the field of data technologies used in the smart cities' context and some background terminology. In Section 3, we describe the methodology of the study. In Section 4, the results of the bibliometric analysis are presented and discussed.