# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
## 5 Dataset
### Dataset 1
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attributes** & **Type** & **Kind** & **Attributes illustration** \\ \hline Age & Range & Numeric & \\ \hline Job & Set & Categorical & “management”, “housemaid”, “entrepreneur”, “student”, “blue-collar”, “self employed”, “retired”, “technician”, “services” \\ \hline Marital & Set & Categorical & marital status (\"married”, “divorced”, “single”; note: “divorced” means divorced or widowed) \\ \hline Education & Set & Categorical & (“unknown”, “secondary”, “primary”, “tertiary”) \\ \hline Default & Flag & Binary (Categorical) & Has defaulted on credit? (Yes/No in binary) \\ \hline Balance & Range & Numeric & Typical annual balance, in euros \\ \hline Housing & Flag & Binary (Categorical) & Has a mortgage loan? (Yes/No in binary) \\ \hline Loan & Flag & Binary (Categorical) & Individual loan? (Yes/No in binary) \# pertaining to the most recent campaign interaction. \\ \hline Contact & Set & Categorical &
\begin{tabular}{c} Kind of contact communication (categorical: “unknown”, “telephone”, “cellular”) \\ \end{tabular} \\ \hline Day & Range & Numeric & Last contact day of the month \\ \hline Month & Set & Categorical & Year’s last month of contact (categorical: “jan”, “feb”, “mar”, \(\ldots\), “nov”, “dec”) \\ \hline Duration & Range & Numeric & Length of the last contact, in seconds \\ \hline Campaign & Range & Numeric & Number of contacts made for this customer during this campaign (includes the last contact) \\ \hline Pdays & Range & Numeric & Number of days since the last contact with the client from a prior campaign (\(-1\) means the client was not previously contacted) \\ \hline Previous & Range & Numeric & Number of contacts performed for this customer prior to this campaign. \\ \hline Poutcome & Set & Categorical & Result of the preceding marketing effort (categorical: “unknown”, “other”, “failure”, “success”) \\ \hline Output & Flag & Binary (Categorical) & Output variable (desired outcome): y—has the customer made a term deposit? (Yes/No in binary) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details about the bank marketing dataset. Likewise, the dataset includes attributes, such as the day of the month, which ranges from 1 to 31, and the duration of the last contact made with the client, which ranges from 0 to 4918 s. However, the Pdays attribute has a domain that ranges from \(-1\) to 871, indicating the number of days that have passed since the client was last contacted during a previous marketing campaign (\(-1\) indicates that the client has not been contacted). Lastly, the previous attribute shows the number of previous contacts made with the client, ranging from 0 to 275. Typically, there are two primary types of methods used for analyzing and modeling data: supervised and unsupervised learning. In supervised learning, the data input must contain both independent predictor characteristics as well as a dependent target attribute whose value must be estimated. The method learns how to create a model that can predict the value of the target attribute based on the predictor attributes. Decision trees and neural networks are some examples of supervised learning. Generally, supervised learning is most suitable for studies where the goal is to predict a specific attribute [54]. Unsupervised learning differs from supervised learning as it does not aim to predict a specific target attribute, but instead treats all attributes equally. The objective of unsupervised learning is to recognize patterns, clusters, or other approaches for differentiating the data, which may help uncover the relationships between the data rather than predict the value of a target attribute. Unsupervised learning involves techniques such as correlation analysis, statistical measures, and cluster analysis [54].