Most cancer patients, including patients with breast cancer, experience multiple symptoms simultaneously while receiving active treatment. Some symptoms tend to occur together and may be related, such as hot flashes and night sweats. Co-occurring symptoms may have a multiplicative effect on patients' functioning, mental health, and quality of life. Symptom clusters in the context of oncology were originally described as groups of three or more related symptoms. Some authors have suggested symptom clusters may have practical applications, such as the formulation of more effective therapeutic interventions that address the combined effects of symptoms rather than treating each symptom separately. Most studies that have sought to identify clusters in breast cancer survivors have relied on traditional research studies. Social media, such as online health-related forums, contain a bevy of user-generated content in the form of threads and posts, and could be used as a data source to identify and characterize symptom clusters among cancer patients. The present study seeks to determine patterns of symptom clusters in breast cancer survivors derived from both social media and research study data using improved K-Medoid clustering. A total of 50,426 publicly available messages were collected from Medhelp.com and 653 questionnaires were collected as part of a research study. The network of symptoms built from social media was sparse compared to that of the research study data, making the social media data easier to partition. The proposed revised K-Medoid clustering helps to improve the clustering performance by re-assigning some of the negative-ASW (average silhouette width) symptoms to other clusters after initial K-Medoid clustering. This retains an overall non-decreasing ASW and avoids the problem of trapping in local optima. The overall ASW, individual ASW, and improved interpretation of the final clustering solution suggest improvement. The clustering results suggest that some symptom clusters are consistent across social media data and clinical data, such as gastrointestinal (GI) related symptoms, menopausal symptoms, mood-change symptoms, cognitive impairment and pain-related symptoms. We recommend an integrative approach taking advantage of both data sources. Social media data could provide context for the interpretation of clustering results derived from research study data, while research study data could compensate for the risk of lower precision and recall found using social media data.