## 2 Literature Review
Using data from public complaints in Bojnourd, Iran, the authors analyzed levels of satisfaction and classified citizen needs by identifying similar complaint patterns based on the frequency, recency, and satisfaction level of each reported complaint. The K-means algorithm was chosen for its straightforwardness, while FCM was utilized to capture overlapping clusters where complaints could belong to various categories. ICA was also applied to find hidden patterns that might be overlooked by conventional clustering methods. This analysis enabled city officials to pinpoint prevalent citizen complaints, assess satisfaction with resolutions, and prioritize issues according to urgency and public significance. The findings indicate that these clustering techniques could effectively categorize public needs and expose trends, assisting policymakers in prioritizing actions to enhance public services. This study underscored the real-world application of data science in urban management by demonstrating how clustering can help match public services with the changing needs of citizens, ultimately boosting community satisfaction and optimizing resource use. [PERSON] et al. [8] presented a visual analytics model to support municipal authorities in managing citizen complaints. The tool was designed to perform map-based visualizations that help officials investigate patterns in citizen requests and complaints, particularly by leveraging geospatial clustering and heatmaps to identify highly affected areas within cities. By employing DBSCAN [9], the tool groups requests by location and frequency, identifying clusters where service issues are concentrated. It also supports hierarchical clustering to reveal subclusters within major complaint areas. By providing such features, the tool was envisioned to enable city officials to prioritize resources and improve response strategies based on visualized citizen feedback. The authors stated that this approach facilitates a better understanding of urban issues, thereby enhancing the decision-making process for public service improvements. [PERSON] et al. [10] conducted a study to prioritize service areas for urban safety and disaster management by analyzing civic complaints using big data analytics. The authors retrieved keywords related to safety and disaster management from unstructured text messages conveying civic complaints to identify major policy demands. They then filtered the dataset using these keywords and performed a panel analysis to investigate the effects of residents' policy demands on safety and disaster management in 16 local jurisdictions. According to the study, policy requirements varied depending on the socio-cultural factors, such as age, gender, economic status, and proportion of migrants. The conclusion was that city governments should provide tailor-made services to address the specific needs of their residents. [PERSON] et al. [11] proposed a decision support system (DSS) for managing complaints in Ethiopia's technical and vocational education and training (TVET) institutions. TVET institutions encountered various challenges in handling complaints due to limited resources and inefficient systems. The proposed DSS integrates a web-based interface and a mobile application, allowing users to submit complaints from anywhere and track their progress in real time. The system notifies users via SMS and enables administrators to prioritize and resolve issues more efficiently. It also includes reporting features for data analysis, helping decision-makers identify trends and improve service quality. The authors suggested that the DSS framework can enhance accountability and responsiveness in TVET institutions, supporting the goals of good governance and citizen satisfaction. [PERSON] et al. [12] developed a framework to improve public service responses and understand community needs by utilizing 137 databases from Bojnourd, Iran, which track and classify citizen complaints.