A computing device incorporated into an implantable medical device, comprising: processing circuitry and memory; wherein the processing circuitry is configured to: accept input data relating to past clinical episodes collected from a patient over time; group the past clinical episodes into one or more classification clusters in accordance with a similarity metric; receive additional input data relating to a new clinical episode collected from the patient; assign the new clinical episode to a classification cluster if the new clinical episode is similar to one or more of the past clinical episodes contained within the classification cluster as determined in accordance with the similarity metric; and, issue an alert or display the new clinical episode on a display if the new clinical episode is not assigned to a classification cluster; wherein the input data and additional input data comprise cardiac electrograms collected from the patient using sensors; wherein past and new clinical episodes are represented by an episode vector in a p -dimensional vector space made up of p numerically valued features reflective of the patient's clinical condition, wherein the p numerically valued features comprise features related to cardiac arrhythmias, p being an integer and wherein the processing circuitry is further configured to: apply a clustering procedure to the past episode vectors to compute clusters that groups the past episode vectors into a plurality of clusters based upon a distance metric in the p -dimensional vector space; and, define the one or more classification clusters as represented by the centroids of clusters of past episode vectors generated by the clustering procedure; and, assign a new episode vector representing a recent clinical episode to the classification cluster that corresponds to the centroid closest to the new episode vector if the distance from the new episode vector to the closest centroid of a classification cluster is less than a specified value; wherein the processing circuitry is further configured to, after a specified period time or after a specified number of new episode vectors have been collected, add the collected new episode vectors to the past episode vectors and re-compute the classification clusters; wherein the processing circuitry is further configured to weight newer past episode vectors more heavily than older past episode vectors in computing the classification clusters. processing circuitry and memory; wherein the processing circuitry is configured to: accept input data relating to past clinical episodes collected from a patient over time; group the past clinical episodes into one or more classification clusters in accordance with a similarity metric; receive additional input data relating to a new clinical episode collected from the patient; assign the new clinical episode to a classification cluster if the new clinical episode is similar to one or more of the past clinical episodes contained within the classification cluster as determined in accordance with the similarity metric; and, issue an alert or display the new clinical episode on a display if the new clinical episode is not assigned to a classification cluster; wherein the input data and additional input data comprise cardiac electrograms collected from the patient using sensors; accept input data relating to past clinical episodes collected from a patient over time; group the past clinical episodes into one or more classification clusters in accordance with a similarity metric; receive additional input data relating to a new clinical episode collected from the patient; assign the new clinical episode to a classification cluster if the new clinical episode is similar to one or more of the past clinical episodes contained within the classification cluster as determined in accordance with the similarity metric; and, issue an alert or display the new clinical episode on a display if the new clinical episode is not assigned to a classification cluster; wherein the input data and additional input data comprise cardiac electrograms collected from the patient using sensors; apply a clustering procedure to the past episode vectors to compute clusters that groups the past episode vectors into a plurality of clusters based upon a distance metric in the p -dimensional vector space; and, define the one or more classification clusters as represented by the centroids of clusters of past episode vectors generated by the clustering procedure; and, assign a new episode vector representing a recent clinical episode to the classification cluster that corresponds to the centroid closest to the new episode vector if the distance from the new episode vector to the closest centroid of a classification cluster is less than a specified value; wherein the processing circuitry is further configured to, after a specified period time or after a specified number of new episode vectors have been collected, add the collected new episode vectors to the past episode vectors and re-compute the classification clusters; wherein the processing circuitry is further configured to weight newer past episode vectors more heavily than older past episode vectors in computing the classification clusters.