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 mark...
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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 shortterm 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. Impact Statement This research work used the FPC and prominent clustering algorithms such for clustering stock market data. The analysis process involves extracting the stock market data of these companies over the past two years and these companies are categorized based on profitability percentage and short-term and long-term price fluctuations. The results demonstrate that profitability, stability, a
Density peaks clustering (DPC) algorithm is a novel algorithm that efficiently deals with the complex structure of the data sets by finding the density peaks. It needs neither iterative process nor more parameters. Th...
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Density peaks clustering (DPC) algorithm is a novel algorithm that efficiently deals with the complex structure of the data sets by finding the density peaks. It needs neither iterative process nor more parameters. The density-distance is utilized to find the density peaks in the DPC algorithm. But unfortunately, it will divide one cluster into multiple clusters if there are multiple density peaks in one cluster and ineffective when data sets have relatively higher dimensions. To overcome the first problem, we propose a fdpc algorithm based on a novel merging strategy motivated by support vector machine. First, the strategy utilizes the support vectors to calculate the feedback values between every two clusters after clustering based on the DPC. Then, it merges clusters to obtain accurate clustering results in a recursive way according to the feedback values. To address the second limitation, we introduce nonnegative matrix factorization into the fdpc to preprocess high-dimensional data sets before clustering. The experimental results on real-world data sets and artificial data sets demonstrate that our algorithm is robust and flexible and can recognize arbitrary shapes of the clusters effectively regardless of the space dimension and outperforms DPC.
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