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Exploring Clustering Patterns: Necessity of Pattern Extraction in Data Analysis

In clustering analysis, especially with samples and terms, it's crucial to group similar patterns together. However, these clusters can become separated in clustering heatmaps, complicating data interpretation. Pattern extraction enhances this process by consolidating all patterned terms effectively, ensuring a comprehensive analysis. This method yields complete results, specifically from PTW extraction, involving 28 key terms. The analysis reveals that certain terms, as outlined in the subsequent table, can be exclusively derived from pattern extraction due to inconsistencies in data magnitude or distribution.

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Exploring Clustering Patterns: Necessity of Pattern Extraction in Data Analysis

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  1. This is also true when clustering both Samples and terms These three clusters with similar patterns shall be clustered together, but are separated in the whole clustering heatmap That is Why we need pattern extraction to get all patterned terms together easily and completely

  2. Complete Result ONLY from PTW Extraction, n=28 terms The following terms in table only Can get from Pattern Extraction (due to the uneven data magnitude Or distribution?)

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