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Data mining and techniques for analyzing big data play a crucial role in various practical fields, including financial markets. However, only a few quantitative studies have been focused on predicting daily stock market returns. The data mining methods used in previous studies are either incomplete or inefficient. This study used the FPC clustering algorithm and prominent clustering algorithms such as K-means, IPC, FDPC, and GOPC for clustering stock market data. The stock market data utilized in this study comprise data from cement companies listed on the Tehran Stock Exchange. These data concerning capital returns and price fluctuations will be examined and analyzed to guide investment decisions. The analysis process involves extracting the stock market data of these companies over the past two years. Subsequently, these companies are categorized based on two criteria: profitability percentage and short-term and long-term price fluctuations, using the FPC clustering algorithm and the classification above algorithms. Then, the results of these clustering analyses are compared against each other using standard and recognized evaluation criteria to assess the quality of the clustering analysis. The findings of this investigation indicate that the FPC algorithm provides more favorable results than other algorithms. Based on the results, companies demonstrating profitability, stability, and loss within short-term (weekly and monthly) and long-term (three-month, six-month, and one-year) time frames will be placed within their respective clusters and introduced accordingly.