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Modern data analysis allows to infer from the data to a very large extent. Particularly noteworthy are techniques based on the detection of anomalies in datasets. Unsupervised approaches with no labeled records are gaining ground. One of the most frequently used and efficient unsupervised methods is Isolation Forest (IF) with its numerous specialized modifications. The considerations of this paper are devoted to the enhancement of Minimal Spanning Tree-Based Isolation Forest (MSTBIF). MSTBIF exhibits high effectiveness. Its evaluation function consists of the sum of two parameters. Hence, the main motivation of the work is a better composition of the MSTBIF evaluation function parameters. Crucial improvements are made in two directions. Firstly, the output parameters related to the tree height and the distance to the nearest tree leaf are normalized. Normalizations take place in a new manner. The properties of created minimal spanning trees are used. Secondly, a simple aggregation of output parameters has been modified. The work introduces an innovative anomaly evaluation scheme. Namely, fuzzy rules prepared in the Takagi-Sugeno inference model are applied. Novel proposal allows for a more precise inference about the occurrence of anomalies in the considered elements. In order to compare the results, the AUC values are measured. A series of experiments with IF, MSTBIF, and new approach are carried out. The research results on the new proposal prove a significant refinement of the anomaly assessment. The improvement over MSTBIF is clearly noticeable. Compared to the basic IF method, the results are better by about 6%.
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