Dementia, characterized by a significant decline in cognitive abilities, encompasses various neurodegenerative disorders. This includes Alzheimer's disease (AD), frontotemporal dementia (FTD) and their clinical manifestation named primary progressive aphasia (PPA), mainly involving language difficulties. In general these diseases exhibit some disease specific clinical characteristics. However, considerable heterogeneity exists in both atrophy patterns and symptom expression within each disease. Additionally, the overlap in disease patterns between AD and FTD complicates diagnosis. In this study, an unsupervised clustering algorithm is used to identify distinct atrophy patterns within AD and FTD, aiming to explore heterogeneity as well as disease overlap. Through the clustering of grey matter volumes, we identified two primary clusters potentially reflective of disease stage. These clusters were externally validated. Additionally, the two primary clusters were subdivided into four distinct clusters, each representing an unique atrophy pattern. These four clusters exhibited differences in cognitive performance in the five cognitive domains: visuospatial functioning, language, memory/learning, processing speed, and attention/executive functioning. The identified clusters provide inside in the atrophy patterns in AD and FTD, as well as their relation to clinical diagnosis and cognitive performance.