In this paper, a novel algorithm (IBC1) for graph clustering with no prior assumption of the number of clusters is introduced. Furthermore, an additional algorithm (IBC2) for graph clustering when the number of clusters is given beforehand is presented. Additionally, a new measure of evaluation of clustering results is given—the accuracy of formed clusters (T). For the purpose of clustering human activities, the procedure of forming string sequences are presented. String symbols are gained by modeling spatiotemporal signals obtained from inertial measurement units. String sequences provided a starting point for forming the complete weighted graph. Using this graph, the proposed algorithms, as well as other well-known clustering algorithms, are tested. The best results are obtained using novel IBC2 algorithm: T = 96.43%, Rand Index (RI) 0.966, precision rate (P) 0.918, recall rate (R) 0.929 and balanced F-measure (F) 0.923.

中文翻译：

---

图聚类的新算法在人类活动中的应用

在本文中，介绍了一种新的图聚类算法（IBC1），该算法无需事先假设聚类的数量。此外，提出了一种额外的算法（IBC2），用于在预先给出聚类数时进行图聚类。此外，给出了一种评估聚类结果的新方法-形成聚类的准确性（T）。出于聚类人类活动的目的，介绍了形成字符串序列的过程。字符串符号是通过对从惯性测量单元获得的时空信号进行建模而获得的。字符串序列为形成完整的加权图提供了一个起点。使用该图，对提出的算法以及其他众所周知的聚类算法进行了测试。使用新颖的IBC2算法可获得最佳结果：T = 96.43％，兰德指数（RI）为0.966，准确率（P）为0.918，召回率（R）为0.929，平衡F量度（F）为0.923。