## 2 Literature Review
The proposed framework integrates (1) data mining techniques, such as the recency, frequency, and monetary value (RFM) model, and (2) clustering algorithms, including K-means [13] and the bees algorithm (BA) [14], to analyze complaint trends. While the K-means method provides a simple and popular clustering technique, BA, inspired by the foraging behavior of bees, offers a more refined approach to optimally cluster citizen needs. The comparison highlights the efficiency of BA in segmenting and prioritizing complaints for actionable insight. Using the framework, the authors were able to successfully identify key areas of public dissatisfaction and prioritize actions based on citizens' needs. The implementation results show that the proposed framework provides valuable feedback for city officials to streamline complaint resolution, enhance service quality, and ultimately boost citizen satisfaction by focusing resources on high-priority needs identified from the data. Such data-driven management approaches appeared to support better public engagement by addressing citizens' concerns more effectively. In [PERSON] et al. [15], a decision-support model powered by a simple additive weighting (SAW) technique for evaluating employee performance was presented. SAW is a multi-criteria decision-making approach that helps organizations assess employee performance by assigning weights to various criteria. SAW allows for a systematic and objective ranking of employees based on their performance across different metrics. The authors demonstrated the practical implementation of SAW in a performance appraisal system, where criteria such as attendance, work quality, and skills are assessed. By scoring and weighting each criterion, the model provides a ranking to identify top performers and those needing improvement, aiding managers in making promotion, training, or development decisions. The implementation results also reveal that the SAW-based model enhances transparency and consistency in evaluations, making it a valuable tool for human resource management in organizations aiming to foster productivity and employee development. Similar to [15], [PERSON] and [PERSON] [16] also proposed a SAW-based decision-support model to prioritize customer complaints effectively. This study specifically focused on evaluating various complaint types by assigning weights to factors such as urgency and customer impact, allowing organizations to determine which complaints require immediate attention. The authors discovered that the SAW-based model provides a straightforward, quantifiable method for complaint prioritization, helping engineers and managers improve response times and increase customer satisfaction. This method also aligns with company goals by focusing on key performance indicators (KPIs) related to complaint resolution, ultimately fostering better customer service and operational efficiency in competitive business environments. [PERSON] et al. [17] proposed a framework to determine the service priority of pavement maintenance on rural highways by employing spatial big data analytics. The authors first datalog the past pavement maintenance records, including the location, duration, and traffic control of various roadworks, such as pothole repair, roadkill treatment, micro surfacing, and emergency infrastructure maintenance, to establish a database. The authors restructured the data to train a machine learning model to estimate the priority of pavement maintenance service. The proposed framework was expected to be deployed to practical uses in helping local governments establish short and mid-term pavement maintenance strategies in rural areas. More recently, [PERSON] et al. [18] presented an advanced decision-support model designed to enhance how e-commerce businesses handle customer complaints, focusing on increasing customer satisfaction and trust. The authors integrated various tools and algorithms to categorize complaints, allocate resources efficiently, and monitor the effectiveness of complaint resolution strategies.