# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
###### Abstract  
Clustering is an effective statistical data analysis technique; it has several applications, including data mining, pattern recognition, image analysis, bioinformatics, and machine learning. Clustering helps to partition data into groups of objects with distinct characteristics. Most of the methods for clustering use manually selected parameters to find the clusters from the dataset. Consequently, it can be very challenging and time-consuming to extract the optimal parameters for clustering a dataset. Moreover, some clustering methods are inadequate for locating clusters in high-dimensional data. To address these concerns systematically, this paper introduces a novel selection-free clustering technique named data point positioning analysis (DPPA). The proposed method is straightforward since it calculates 1-NN and Max-NN by analyzing the data point placements without the requirement of an initial manual parameter assignment. This method is validated using two well-known publicly available datasets used in several clustering algorithms. To compare the performance of the proposed method, this study also investigated four popular clustering algorithms (DBSCAN, affinity propagation, Mean Shift, and K-means), where the proposed method provides higher performance in finding the cluster without using any manually selected parameters. The experimental finding demonstrated that the proposed DPPA algorithm is less time-consuming compared to the existing traditional methods and achieves higher performance without using any manually selected parameters. clustering; parameter-free algorithm; unsupervised learning; data mining; DPPA +
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learning must also contain noise and outliers. Non-Gaussian noise is particularly prevalent in applications involving measurements. On the other hand, outliers are incongruuous observations with the overall population. These idiosyncrasies provide significant hurdles for issues involving both linear and non-linear systems, especially when coupled with specific output requirements. Effective investigations into the consequences of these extra concerns are found in several recent works, e.g., [6; 7]. Moreover, the raw data are often in crude formats. Clustering techniques need a preprocessing phase to deal with high dimensions and undesirable sampling problems. In circumstances involving large dimensions, preprocessing strategies [8; 9; 10] have been proposed to improve performance. To observe the dataset more precisely and make it more suited for subsequent processing, these methods often restructure the sample space using transformations or eliminations. For example, the principal component analysis (PCA) converts sample characteristics into a form with the largest variance, making it more appropriate for classification tasks with the added bonus of decreased dimensions [11]. This idea may be immediately extended as feature transformation in general. It is crucial to remember that these strategies keep all traits and restructure them using (non-linear) combinations rather than discard unimportant ones. The expanded method of dimensionality reduction by feature selection eliminates unimportant features and considers the subset of features that seems to be the most crucial while adhering to predetermined optimality criteria. Future classification or clustering problems may benefit from using any of these strategies to provide more accurate representations. Despite the fact that feature selection may be utilized as an efficient solution to high-dimensional challenges, the elimination process may result in a loss of critical information with great significance in diverse contexts. After considering the difficulties of handling high-dimensional data, it is vital to list a few methods for avoiding overfitting. To address the issue of high-dimensional datasets, researchers have proposed various techniques. For instance, [PERSON] et al.