# Rough IPFCM Clustering Algorithm and Its Application on Smart Phones with Euclidean Distance
###### Abstract  
New interval clustering technology for symbolic data analysis (SDA) on smart phones is shown to be beneficial for mobile computing devices for smart data analysis in this paper. A new interval clustering method that combined the rough set with interval possibilistic fuzzy C-means (IPFCM) algorithm under Euclidean distance is proposed and implemented on smart phones. Symbolic clustering algorithms (SCAs) have been widely used for pattern recognition, data mining, artificial intelligence, etc. In general, the SCA is unsupervised classification that is divided into groups according to symbolic data sets. However, the traditional interval fuzzy C-means (IFCM) clustering method still has noisy and data overlapping problems associated with these symbolic interval data. Hence, a new rough set with the interval possibilistic fuzzy C-means (RIPFCM) clustering algorithm with Euclidean distance was proposed to address the symbolic interval data (SID). That is, the proposed method can perform better than the traditional IFCM clustering algorithm for SID clustering in noisy environments and with data overlapping problems. The new RIPFCM algorithm under the Euclidean distance method was proposed to deal with SID on new applications in smart phones. Consequently, this method shows the expansion of the smart phone's computing power and its future application in new SDA. r 2022 2022 2022 2022 2022 2022 202  
uses C#, which is compatible on AASMA for program development [4]. Hence, in this paper, we use C# language for the proposed interval clustering method to develop Windows mobile software applications. The era of the \"touchscreen generation economy\" is coming, and professional data analysis can be done at any time at the touch of a finger. Therefore, the development of AI methods on mobile platforms is currently a hot research field [5; 6]. [PERSON] et al. [7] proposed the evaluation of machine learning and web-based processes for damage score estimation of existing buildings. [PERSON] et al. [8] proposed precision irrigation systems using sensor network technology integrated with iOS/Android applications. [PERSON] et al. [9] proposed the application of iOS/Android-based assessment and monitoring systems for building inventory under seismic impact. [PERSON] et al. [10] proposed a prototype for the machine learning-based earthquake hazard safety assessment of structures by using a smartphone app. In general, new interval clustering technology using the symbolic data analysis (SDA) method on smart phones is beneficial to mobile computing devices for smart data analysis. However, few studies have been published on this topic [11]. Hence, combining the rough set with the interval possibilistic fuzzy C-means (RIPFCM) algorithm under Euclidean distance is proposed and is implemented on smart phones in this paper. Symbolic clustering algorithms have been widely used for pattern recognition, data mining, artificial intelligence, etc. Therefore, this paper proposes a new interval fuzzy C-means clustering method to address symbolic interval data and the expansion of smart phones' computing power for new symbolic data analysis. That is, SDA and the expansion of smart phones' computing power is an important research topic in mobile computing and data analysis. The symbolic interval clustering algorithm involves unsupervised interval clustering that is divided into groups according to symbolic data sets. That is, analysis of interval data under fuzzy analysis requires a Type II fuzzy set [12] or interval fuzzy C-means (IFCM) clustering method [13]. [PERSON] [13] extended the fuzzy C-means (FCM) clustering method and proposed an IFCM clustering method for symbolic interval data (SID) to conduct SDA. [PERSON] et al. [14; 15] proposed an interval competitive agglomeration clustering algorithm. In general, the IFCM clustering method was proposed to deal with SID [13]. However, outliers and noisy problems are still associated with these SID. [PERSON] et al.