Cluster analysis is a major method in data mining to present overviews of large data sets. Clustering methods allows dimension reducing by finding groups of similar objects or elements. Visual cluster analysis has been defined as a specialization of cluster analysis and is considered as a solution to handle complex data using interactive exploration of clustering results. In this chapter, we consider three cases studies in order to illustrate cluster analysis and interactive visual analysis. The first case study is related to information retrieval field and illustrates the case of multi-dimensional data in which objects to analyze are represented considering various features or variables. Evaluation in information retrieval considers many performance measures. Cluster analysis is used to reduce the number of measures to a small number that can be used to compare various search engines. The second case study considers networks in which data to analyze is represented in the form of matrices that correspond to adjacency matrices. The data we used is obtained from publications; cluster analysis is used to analyze collaborative networks. The third case study is related to curve clustering and applies when temporal data is involved. In this case study, the application is time series gene expression. We conclude this chapter by presenting some other types of data for which visual clustering can be used for analysis purposes and present some tools that implement other visual analysis functionalities we did not present in the case studies.