COBWEB is an incremental conceptual clustering system that organizes data to maximize inference ability. It is computationally efficient and can be applied flexibly across various domains. The system is designed to address real-world constraints by incrementally processing observations, which allows for continuous learning and adaptation. COBWEB uses a hill-climbing search strategy to build classification trees, where each node represents a probabilistic concept. The system evaluates the quality of classifications using a heuristic measure called category utility, which balances intra-class similarity and inter-class dissimilarity. This measure is based on probabilities derived from attribute-value pairs and helps determine the best classification for new objects. COBWEB incorporates objects into classification trees by classifying them along an appropriate path, updating counts, and applying one of several operators. These operators include placing an object in an existing class, creating a new class, combining classes, or dividing classes. The system also includes operators for merging and splitting nodes to adjust the classification tree based on new information. These operators allow COBWEB to move bidirectionally through a space of possible hierarchies, improving its ability to adapt to changing data. COBWEB's effectiveness as an incremental learner was demonstrated through experiments in domains such as soybean disease cases and congressional voting records. In the soybean domain, COBWEB successfully identified diagnostic conditions and predicted attribute values with high accuracy. The system's ability to infer diagnostic conditions and predict attribute values was validated through tests that showed high success rates compared to simpler frequency-based methods. These results indicate that COBWEB's classification trees are effective for inference tasks, particularly in domains with significant data dependencies. COBWEB's incremental nature allows it to process data continuously, making it suitable for real-world applications where data is available incrementally. The system's ability to adapt to new information and its computational efficiency make it a robust learner. However, the system's performance can be affected by the initial data distribution and the complexity of the domain. Despite these challenges, COBWEB's use of category utility and hill-climbing strategies ensures that it can effectively learn and adapt to new data, making it a valuable tool for conceptual clustering and inference tasks.COBWEB is an incremental conceptual clustering system that organizes data to maximize inference ability. It is computationally efficient and can be applied flexibly across various domains. The system is designed to address real-world constraints by incrementally processing observations, which allows for continuous learning and adaptation. COBWEB uses a hill-climbing search strategy to build classification trees, where each node represents a probabilistic concept. The system evaluates the quality of classifications using a heuristic measure called category utility, which balances intra-class similarity and inter-class dissimilarity. This measure is based on probabilities derived from attribute-value pairs and helps determine the best classification for new objects. COBWEB incorporates objects into classification trees by classifying them along an appropriate path, updating counts, and applying one of several operators. These operators include placing an object in an existing class, creating a new class, combining classes, or dividing classes. The system also includes operators for merging and splitting nodes to adjust the classification tree based on new information. These operators allow COBWEB to move bidirectionally through a space of possible hierarchies, improving its ability to adapt to changing data. COBWEB's effectiveness as an incremental learner was demonstrated through experiments in domains such as soybean disease cases and congressional voting records. In the soybean domain, COBWEB successfully identified diagnostic conditions and predicted attribute values with high accuracy. The system's ability to infer diagnostic conditions and predict attribute values was validated through tests that showed high success rates compared to simpler frequency-based methods. These results indicate that COBWEB's classification trees are effective for inference tasks, particularly in domains with significant data dependencies. COBWEB's incremental nature allows it to process data continuously, making it suitable for real-world applications where data is available incrementally. The system's ability to adapt to new information and its computational efficiency make it a robust learner. However, the system's performance can be affected by the initial data distribution and the complexity of the domain. Despite these challenges, COBWEB's use of category utility and hill-climbing strategies ensures that it can effectively learn and adapt to new data, making it a valuable tool for conceptual clustering and inference tasks.