# Study on the Technology Trend Screening Framework Using Unsupervised Learning
## 3 Proposed Methodology
### Technology Trend Screening Using Outlier Detection and Topic Model  
This section will describe the proposed technology trend screening method based on a machine learning algorithm. Figure 4 shows the proposed approach. The key to our approach is to obtain information in outliers and present how to apply that in technology management.  
This study utilizes the document embedding results performed in Section 3.2 for technology trend screening. An outlier detection method is applied to distinguish mainstream and non-mainstream technologies in document embedding results. Three techniques, as shown in Figure 3, are applied to find outliers to enhance the accuracy of the analysis: (1) finding the outlier using interquartile range(IQR) (2) finding the outlier using the three-sigma rule (3) finding the outlier using iForest.  
The outlier detection method using IQR, IQR can be calculated using Equation (6). Q1 and Q3 refer to the first and third quartiles, respectively.  
\[\text{IQR}=\text{Q}_{3}-\text{Q}_{1} \tag{6}\]  
If an observation value is smaller than \(\text{Q}_{1}-1.5\times\text{IQR}\), or larger than \(\text{Q}_{3}+1.5\times\text{IQR}\), the value is determined to be an outlier.  
The technique to find outliers using the three-sigma rule is described as follows. When the distribution of data is a normal distribution, an outlier is determined if it deviates from Equation (7). \(\mu\) and \(\sigma\) refer to mean and standard deviation, respectively.  
\[\mu\pm 3\sigma \tag{7}\]  
iForest, a machine learning-based outlier detection algorithm proposed by [PERSON] et al., is executed based on the regression decision tree [30]. In iForest, because normal data requires recursive binary partitioning, which is not true for outliers, there is an assumption that its depth is close to the root node.  
Non-mainstream outliers are selected through voting for the set of outliers derived through each outlier detection technique. LDA is utilized to identify technical topics for the selected set of outliers. Table 3 shows the hyperparameters for the LDA model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Candidates** \\ \hline Vector size & 2 \\ Window & 10 \\ Minimum count & 2 \\ Epoch & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters for the Doc2 vec model.  
Figure 4: Concept for the Technology Trend Screening.  
Perplexity is one of the measures for evaluating a probabilistic model, which determines how well a model performs a prediction for a given dataset [29]. Because LDA is one of the representative probabilistic models, it can be evaluated using perplexity. A lower perplexity value represents a better generalization performance. When there are \(M\) document test sets, perplexity can be obtained by using Equation (8).  
\[perplexity(D_{test})=exp\left\{-\frac{\sum_{d=1}^{M}\log p(w_{d})}{\sum_{d=1}^{ M}N_{d}}\right\} \tag{8}\]  
As mentioned above, perplexity is primarily identified to determine the optimal number of topics. Therefore, the lowest perplexity value can be considered optimal \(k\).