Surveillance of crowded public spaces and detection of anomalies from the video is important for public safety and security. While anomaly detection is possible by detection and tracking of individuals in low-density areas, such methods are not reliable in high-density crowded scenes. In this work we propose a holistic unsupervised approach to cluster different behaviors in high density crowds and detect the local anomalies using these clusters. Finite-Time Lyapunov Exponents (FTLE) is used for analyzing the crowd flow and this flow data is clustered by agglomerative hierarchical clustering. To detect if there is any anomaly in the video, mean of maximum values for pixels in each cluster is used and skewness value of the clusters are calculated. An adaptive threshold is calculated using equal width thresholding which is subsequently used to determine abnormal clusters which are not coherent with the general flow. The method does not require any user defined thresholds or preset rules. Publicly available datasets and our own dataset (which is also made publicly available) are used for testing and demonstrating the effectiveness of the proposed method.