In this article, we evaluate the efficiency and performance of two clustering algorithms: $AHC$ (Agglomerative Hierarchical Clustering) and $K-Means$. We are aware that there are various linkage options and distance measures that influence the clustering results. We assess the quality of clustering using the Davies–Bouldin and Dunn cluster validity indexes. The main contribution of this research is to verify whether the quality of clusters without outliers is higher than those with outliers in the data. To do this, we compare and analyze outlier detection algorithms depending on the applied clustering algorithm. In our research, we use and compare the $LOF$ (Local Outlier Factor) and $COF$ (Connectivity-based Outlier Factor) algorithms for detecting outliers before and after removing $1\%$, $5\%$, and $10\%$ of outliers. Next, we analyze how the quality of clustering has improved. In the experiments, three real data sets were used with a different number of instances.

The aim of the article is the analysis of using LOF, COF and Kmeans algorithms for outlier detection in rule based knowledge bases. The subject of outlier mining is very important nowadays. Outliers in rules mean unusual rules which are rare in comparison to others and should be explored further by the domain expert. In the research the authors use the outlier detection methods to find a given (1%, 5%, 10%) number of outliers in rules. Then, they analyze which of seven various quality indices, that they used for all rules and after removing selected outliers, improve the quality of rule clusters. In the experimental stage the authors used six different knowledge bases. The results show that the optimal results were achieved for COF outlier detection algorithm as the one for which, among all analyzed quality indices, the cluster quality improved most frequently.

Earlier studies have indicated that consumption of beavers Castor ssp. by wolves Canis lupus varies seasonally and is influenced by rainfall affecting water levels. Therefore, to determine whether these carnivores prey more often on beavers in drier seasons and years, we assessed the diet of wolves in Wigry National Park (NE Poland) by analysing 303 scats collected from 2017 to 2019. The most important prey of wolves in this region was the wild boar Sus scrofa (25.2% of consumed biomass), Eurasian beaver Castor fiber (24.4%) and roe deer Capreolus capreolus (24.2%). Predator niche breadth was broader in spring-summer (B = 2.24) than in autumn-winter (B = 1.32), mainly due to higher consumption of beavers (37.3% in spring-summer and 10.7% in autumn-winter). Wolves consumed more beavers (40.9%) and had the broadest niche breadth (B = 1.99) in 2019, when the annual rainfall was lowest. Our study provides further evidence that wolves prey on beavers more often in ice-free seasons and drier years.

Rule-based knowledge bases are constantly increasing in volume, thus the knowledge stored as a set of rules is getting progressively more complex and when rules are not organized into any structure, the system is inefficient. The aim of this paper is