Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Although abundant expert knowledge exists in many areas where unlabelled data is examined, frequently such knowledge is not incorporated into automatic analysis. Semi-supervised learning allows for the incorporation of additional knowledge with the help of labels or constraints. However it is the field of supervised learning and the recently proposed advanced paradigm of learning using privileged information that provides an intriguing concept of incorporating special type of additional knowledge. In this thesis we explore the question of importance and incorporation of such additional knowledge within unsupervised learning. Our analysis is performed from four different viewpoints, namely anomaly detection, cluster interpretation, visualisation and identification. The functionality of signal fusion and low-level pattern recognition in the human immune system is our inspiration. A more practical set of immunology derived techniques is developed, allowing for the fusion of additional information for improve
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