Data controllers manage immense data, and occasionally, it is released publically to help the researchers to conduct their studies. However, this publically shared data may hold personally identifiable information (PII) that can be collected to re-identify a person. Therefore, an effective anonymization mechanism is required to anonymize such data before it is released publically. Microaggregation is one of the Statistical Disclosure Control (SDC) methods that are widely used by many researchers. This method adapts the k-anonymity principle to generate k-indistinguishable records in the same clusters to preserve the privacy of the individuals. However, in these methods, the size of the clusters is fixed (i.e., k records), and the clusters generated through these methods may hold non-homogeneous records. By considering these issues, we propose an adaptive size clustering technique that aggregates homogeneous records in similar clusters, and the size of the clusters is determined after the semantic analysis of the records. To achieve this, we extend the MDAV microaggregation algorithm to semantically analyze the unstructured records by relying on the taxonomic databases (i.e., WordNet), and then aggregating them in homogeneous clusters. Furthermore, we propose a distance measure that determines the extent to which the records differ from each other, and based on this, homogeneous adaptive clusters are constructed. In experiments, we measured the cohesiveness of the clusters in order to gauge the homogeneity of records. In addition, a method is proposed to measure information loss caused by the redaction method. In experiments, the results show that the proposed mechanism outperforms the existing state-of-the-art solutions.