Horizontal collaborative clustering is such a clus-tering method that carries clustering on one data set describing a pattern set in one feature space with col-laborative introducing of outer partition information obtained by clustering on another data set but de-scribing the same pattern set in another feature space. In order to implement the collaborative clustering, horizontal collaborative fuzzy c-means (HC-FCM) was proposed by W. Pedrycz. In HC-FCM, the outer partition matrix is incorporated with the objective function in FCM. This manner of making use of the outer partition matrix emphasizes on the use of total collaborative clustering information provided by the outer partition matrix, thus this method can be called completely horizontal collaborative fuzzy c-means (CHC-FCM). In reality, on many occasions of col-laborative clustering, we may be interested only in the cluster information of some special patterns, say the patterns with distinct membership for example. In this paper, we implement the horizontal collabora-tive clustering with the partial supervision clustering approach where the clustering is carried by the guid-ance of some labeled patterns. In this approach, we can select the patterns we are interested in to provide FCM with collaborative information and control the degree of the influence of the selected patterns on the clustering. This new method is called partially hori-zontal collaborative fuzzy c-means (PHC-FCM). Af-ter presenting two approaches to realizing the selec-tion of the labeled patterns, named cut-set based ap-proach and entropy based approach, we give the de-tailed algorithm of PHC-FCM. Experiments are car-ried and show the performance of the new method.