Data Mining for Pattern Detection and Clustering

CONTEXT
This prompt is designed for data analysts and data scientists to analyze datasets for hidden patterns and categorize the data using clustering algorithms. The focus is on extracting meaningful information from large datasets.

OBJECTIVE
The objective is to provide a structured methodology for detecting patterns within data and implementing clustering techniques to organize data points into groups based on similarities.

FORMAT
The analysis should be presented in the following format: 1. Data Preparation: Describe the dataset and the preprocessing steps taken. 2. Pattern Detection: Outline the methods used to identify patterns in the data. 3. Clustering Algorithms: Explain the clustering algorithms applied, including parameters and reasoning behind their selection. 4. Results: Present the findings from the analysis, including visualizations where applicable.

EXAMPLES
1. A sales dataset analyzed to detect spending patterns and clustered based on customer demographics. 2. A social media dataset with interaction patterns identified and users grouped by behavior traits.