## 4 Analysis, Results, and Discussion
### Stage 2--Data Analysis Using WEKA
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Node Name** & **Degree** & **Frequency** & **Betweenness** & **Topic** & **Conductivity** & **Locality** & **Diversity** \\ \hline Sum Total & 648 & 134 & 2.691812 & n/a & 1749.7 & 64 & 8038.2 \\ Sum/74 Nodes & 8.76 & 1.81 & 0.036376 & n/a & 23.64 & 0.86 & 108.62 \\ site & 43 & 11 & 0.675038 & 2 & 157 & 0 & 613.7 \\ research & 16 & 4 & 0.25 & 0 & 156.3 & 0 & 625 \\ heritage & 38 & 9 & 0.221461 & 2 & 58.3 & 1 & 246.1 \\ resilient & 24 & 6 & 0.189498 & 0 & 79 & 0 & 315.8 \\ plan & 24 & 4 & 0.161752 & 0 & 67.4 & 0 & 404.4 \\ machine & 9 & 3 & 0.126712 & 3 & 140.8 & 0 & 422.4 \\ change & 23 & 7 & 0.122146 & 1 & 53.1 & 0 & 174.5 \\ climate & 25 & 6 & 0.11035 & 1 & 44.1 & 1 & 183.9 \\ coastal & 16 & 4 & 0.082572 & 2 & 51.6 & 0 & 206.4 \\ learning & 8 & 3 & 0.079909 & 3 & 99.9 & 0 & 266.4 \\ make & 11 & 2 & 0.064688 & 2 & 58.8 & 0 & 323.4 \\ difference & 6 & 1 & 0.055524 & 6 & 92.5 & 0 & 555.2 \\ approach & 11 & 3 & 0.055175 & 0 & 50.2 & 0 & 183.9 \\ main & 6 & 1 & 0.054604 & 6 & 91 & 0 & 546 \\ hazard & 11 & 2 & 0.050609 & 2 & 46 & 0 & 253 \\ case & 11 & 2 & 0.048896 & 4 & 44.5 & 0 & 244.5 \\ adaptation & 15 & 3 & 0.046423 & 0 & 30.9 & 0 & 154.7 \\ impact & 13 & 4 & 0.045282 & 1 & 34.8 & 0 & 113.2 \\ represent & 6 & 1 & 0.040049 & 2 & 66.7 & 0 & 400.5 \\ method & 10 & 2 & 0.031963 & 0 & 32 & 0 & 159.8 \\ highlight & 6 & 1 & 0.019914 & 6 & 33.2 & 0 & 199.1 \\ data & 10 & 2 & 0.019692 & 0 & 19.7 & 1 & 98.5 \\ factor & 6 & 1 & 0.018994 & 6 & 31.7 & 0 & 189.9 \\ classifying & 6 & 1 & 0.016933 & 6 & 28.2 & 0 & 169.3 \\ range & 6 & 1 & 0.016584 & 6 & 27.6 & 0 & 165.8 \\ gathering & 4 & 1 & 0.013604 & 3 & 34 & 0 & 136 \\ general & 6 & 1 & 0.013413 & 2 & 22.4 & 0 & 134.1 \\ technique & 5 & 1 & 0.013413 & 3 & 26.8 & 0 & 134.1 \\ specific & 6 & 1 & 0.012652 & 2 & 21.1 & 0 & 126.5 \\ resilience & 6 & 1 & 0.010306 & 6 & 17.2 & 0 & 103.1 \\ algorithm & 6 & 1 & 0.009957 & 6 & 16.6 & 0 & 99.6 \\ adapt & 10 & 2 & 0.009513 & 0 & 9.5 & 1 & 47.6 \\ propose & 6 & 1 & 0.001522 & 6 & 2.5 & 2 & 15.2 \\ responsible & 6 & 1 & 0.001522 & 6 & 2.5 & 2 & 15.2 \\ renewable & 6 & 1 & 0.000381 & 2 & 0.6 & 9 & 3.8 \\ resource & 6 & 1 & 0.000381 & 2 & 0.6 & 9 & 3.8 The pre-processing shows the imported dataset using a bar graph, and the main features of the data processing tools can be summarized as follows:  
* Classification and regression algorithms;
* Clustering algorithms;
* Algorithms to find association rules;
* Evaluators' and researchers' algorithms. The basic purpose of using the WEKA software is to discover the common patterns of adapting coastal heritage sites in relation to climate change, in terms of solutions and priorities, to support designing a resilience approach for such cases in Egypt. This section highlights the main algorithms that can be used for the resilience range and the order of arrangement in the implementation. According to its predictive properties and each case, a machine learning method was used to obtain the maximum benefit from the huge amount of data in the Internet Cloud in relation to this subject. In this process, the data are passed through a pre-processing step. Then, association rules are determined to discover the main related components and their associated elements, which can be achieved either using visual tools or by programming attribute commands. To fulfill this operation, as shown in Figure 13, a the following set of procedures can be used. * Data are transformed through Infra Nodus, and then, the CSV extraction files are re-entered into WEKA. * In data mining, the software applies, through its algorithms, a set of steps to find the pattern and range. * Pattern evaluation is used to review the final output qualitatively according to similar cases of analysis. WEKA supports certain types of files, such as ARFF (Attribute-Relation File Format) and CSV (Comma-Separated Value). Figure 12: WEKA explorer interface used to analyze the preliminary pre-processed data, which resulted from the Infra Nodus output.