**6. Comparing performances algorithms**

Algorithms can be compared in previous experiences using the following criteria:

Root Mean Squared Error (RMSE): The evaluation metric used by all algorithms of clustering is RMSE. RMSE is calculated by the root of the averaging all squared errors between the original data (*X*) and the corresponding predicted values data (*X*). ̅

$$RMSE = \sqrt{\frac{\sum\_{k=1}^{n} \sum\_{i=1}^{c} (\mathbf{x}\_{ik} - \overline{\mathbf{x}}\_{ik})}{n}} \tag{45}$$

where *n* is the total number of patterns in a given data set and *c* is the number of clusters; *xik and xik* the actual and predicted rating values data respectively.

Accuracy is one metric for evaluating classification models. Informally, accuracy is the fraction of predictions the model got right. Formally, accuracy has the following definition:

$$Accuracy = \frac{number\ of\ correct\ samples}{total\ number\ of\ samples} \ast 100\tag{46}$$

#### **Acknowledgements**

This work was supported by Institute of Korea Evaluation Institute of Industrial Technology (KEIT, Next Generation Artificial Intelligence Semiconductor R&D Program) grant funded by the Korea government (Ministry of Trade, Industry & Energy, MOTIE) (Project No. 20010098, Development of Mixed Signal SoC with complex sensor for Smart Home Appliances).

### **Author details**

JaeHyuk Cho Department of Electronic Engineering, Soongsil University, Seoul, Korea

\*Address all correspondence to: chojh@ssu.ac.kr; wogur900@gmail.com

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Data Clustering for Fuzzyfier Value Derivation DOI: http://dx.doi.org/10.5772/intechopen.96385*
