Unsupervised learning for feature discovery employs algorithms designed to identify relevant features or patterns inherent in raw or unlabeled data. These features pertain to unique patterns or hidden characteristics that aren't readily noticeable or understood within the data. In terms of machine learning, 'unlabeled' precedes datasets without a designated target or outcome variable. Therefore, this type of learning leverages computational techniques to uncover and learn the underlying structure from such data.

• Autonomy: These methodologies function independently, without requiring any human intervention or explicit instruction.
• Pattern Recognition: Unsupervised learning excels in detecting subtle patterns in large datasets, facilitating the categorization and comprehension of future data.
• Scalability: Algorithms associated with unsupervised learning can scale efficiently to handle large volumes of data.
• Versatility: These methods have broad applicability, from sentiment analysis to customer segmentation and anomaly detection.

Effective implementation of unsupervised learning techniques involves several stages. Initial steps include the cleansing and pre-processing of data and the selection of an appropriate algorithm-based on specific task requirements. Following this, the algorithm is trained on the data, leading towards the much-desired feature discovery. Evaluation strives to ensure quality, and adjustments are made in the model as required.

There are several key advantages that these methodologies provide:

• Revealing Hidden Correlations: Unsupervised learning algorithms can discover previously unknown relationships or structures within the data that might not be immediately evident.
• Cost-Effective: Data labeling for supervised learning algorithms can be a costly and time-consuming process. By employing unlabeled data, unsupervised learning can minimize these costs substantially.
• Flexibility: It adjusts to changes in the data autonomously and in real-time, allowing the continuous evolution of models with the introduction of new data.
• Scalability: Unlike supervised learning methods which may require extensive computational resources, unsupervised learning typically scales efficiently with large and complex datasets.

Despite its many benefits, unsupervised learning presents a few potential disadvantages:

• Interpretability: The patterns discovered through unsupervised learning can often be challenging to interpret which promotes ambiguity.
• Lack of Precision: Unlike supervised algorithms, unsupervised learning may not always provide precise predictions.
• Dependency on Quality of Data: The effectiveness and accuracy of unsupervised learning algorithms are highly dependent on data quality. Erroneous or irrelevant data can significantly deter feature discovery.

To conclude, the potential of unsupervised learning for feature discovery is immense, given the rising thrust on pattern detection, data interpretation, and decision-making. However, the practice necessitates a well-planned strategy along with a careful analysis of needs and challenges for successful implementation. Therefore, deployment should be closely monitored, and essential adjustments must be made regularly to meet the organization's specific requirements optimally.