Today, we're turning our focus towards comparing various unsupervised learning methods. Our comparative study will include K-means, DBSCAN, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and t-SNE.

Utilizing the Iris flower dataset, we will employ Python's scikit-learn library. Each of these methods possesses unique attributes, so understanding their comparative performance would enable us to choose the one best suited for any given scenario. Let's get started!

In our exploration of unsupervised learning, we've familiarized ourselves with a variety of clustering and dimensionality reduction techniques. Although these techniques share the primary aim of discovering the underlying data structure, the methodologies they use to achieve this can vary significantly. That's where the need for comparison arises, as it helps us select the most suitable technique for a specific problem.

Several metrics, such as accuracy, simplicity, computational efficiency, and interpretability, enable us to compare these techniques. In the following sections, we'll compare clustering and dimension reduction methods using these metrics.

Let's begin by refreshing our memory on the properties of our clustering techniques. K-means is a partition-based technique. It partitions observations into clusters in such a way that each observation belongs to the cluster with the nearest mean. The clusters formed by K-means tend to be spherical, which suits well-spaced, round clusters. However, it doesn't handle noise and outliers effectively and struggles with non-spherical and similarly sized clusters.

In contrast, DBSCAN is a density-based clustering algorithm. It considers clusters as dense regions separated by regions of lower density in the feature space — hence, it can capture clusters of arbitrary shapes, a clear advantage over K-means. Moreover, it can handle noise in the data. However, deciding appropriate parameters such as eps and min_samples can be a bit tricky, and this algorithm may struggle with clusters of differing densities.

K-means and DBSCAN, as clustering techniques, can be compared across several parameters:

• Cluster Quality: K-means excels in creating spherical and similarly sized clusters, while DBSCAN outperforms it in forming clusters of varying shapes and sizes.
• Scalability: While K-means easily scales with large datasets, DBSCAN often requires additional computational resources as the dimensions increase.
• Tolerance to Noise: DBSCAN identifies and handles noise and outliers effectively, giving it an advantage over K-means, which often absorb noisy points into clusters.
• Interpretability: K-means provides intuitive and easy-to-interpret results, while DBSCAN’s results may be slightly harder to interpret.

The above comparison between K-means and DBSCAN will make it easier to decide which method meets your specific requirements. For instance, if your data contains noise or necessitates flexible cluster shapes, DBSCAN may offer a more suitable choice.