## 1 Introduction  
Buildings are responsible for approximately 36% of all CO\({}_{2}\) emissions in the European Union and 28% on the global level. Therefore, the EU has set the goal of developing a sustainable, competitive, secure, and decarbonised energy system by the year 2050 [1, 2]. One of the ways of achieving this goal is through the digitalisation of energy systems and buildings. The EU expects that smart-ready energy systems and buildings are going to offer new opportunities for energy savings. In order to promote smart ready technologies for the building sector, the EU has introduced a smart readiness indicator (SRI). The purpose of SRI is to determine the capability of buildings in using the information and communication technologies to adapt the building operation to the needs of the occupants and the grid while improving the overall performance of the building [1]. Efforts of increasing the building smartness will also increase the number of sensors in buildings, which will, as aresult, create massive amounts of building operational data. Processing and combining this data further can increase its value by providing new knowledge [3]. For instance, building operational data is reflecting the interaction between occupants [4; 5], the building (with its systems) and the environment. The outdoor and indoor environment, the building's energy consumption and the system operation are usually monitored with sensors and meters, but the occupancy pattern and behaviour are not. Collected data processed using data mining techniques can provide information about building occupancy. Understanding the occupancy dynamics of the building, with other previously mentioned building variables creates knowledge that can be used for tasks such as energy, space and facility management, or even as an aid to the organisation's human resources department. For example, in the field of building simulation, occupant behaviour was recognised as a significant factor contributing to the high discrepancy between simulation prediction and real energy use [5; 6; 7; 8]. Furthermore, [PERSON] et al. [9] have listed ten challenges of building simulations where information on occupancy schedule is directly affecting three of them (addressing the building performance gap, modelling human-building interactions and energy model calibration). Data mining is defined as \"the process of discovering insightful, interesting, and novel patterns, as well as descriptive, understandable, and predictive models from large-scale data\" [10]. Data mining is a multi-disciplinary subject that combines methods from statistics, machine learning and artificial intelligence. It can be divided into two distinct groups: supervised and unsupervised analysis. The supervised analysis is the one that uses a training dataset in which both input and output variables are available. By using regression or classification algorithms, the supervised analysis describes quantitative or qualitative relationships between input and output variables. After the supervised model was developed (trained) on training data, it can be applied to a new dataset, containing only inputs while predicting the output data. On the other hand, unsupervised analysis is done on a dataset that includes only inputs, while the target is unknown. It focuses on exploring the correlations, associations, and patterns in the dataset, and in that way, unsupervised analysis can discover new knowledge [11]. Using building operational data with both supervised and unsupervised data mining methods to find more information on occupancy and its influence on building operation, has already been part of various studies, e.g., [PERSON] et al. [12], [PERSON] et al. [13], [PERSON] et al. [14]. Building operational data used in those studies was typically electricity usage, measured occupancy or indoor climate measurements.