I will be doing some data mining soon and want to explore data without creating models for getting to a particular area. Entities like a genome have many different dimensions and I'd like to see data/statistics in a way that I can identify starting points.
Principal Component Analysis (PCA) is not a clustering method but sometimes it helps to reveal clusters when you have many dimensions. You can use PCA for dimensionality reduction as a feature extractor, and use a clustering algorithm at your choice, such as k-means, DBSCAN or OPTICS to visualize the clusters.
Please see this link and check if it contain information u need: http://dl.acm.org/citation.cfm?id=1570040
In this paper we use genetic programming (GP) for feature selection in binary classification tasks. Mathematical expressions built by GP transform the feature space in a way that the relevance of subsets of features can be measured using a simple relevance function. We make some modifications to the standard GP to make it explore large subsets of features when necessary. This is done by increasing the depth limit at run-time and at the same time trying to avoid bloating and overfitting by some control mechanism. We take a filter (non-wrapper) approach to exploring the search space.
To explore data without knowledge about data label, you will need to use clustering algorithm. If you have data label, you can use classification algorithm. I don't know how many class that you have, so I can't sugest a classification/clustering algorithm.
Because of many differents dimensions, maybe you will need an dimension reduction algorithm. If you want to keep your initial features, it is better if you use a feature selection algorithm. If not, you can use a feature extraction algorithm.
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