Distributed clustering algorithm for large scale clustering problems
Clustering is a task which has got much attention in data mining. The task of finding subsets of objects sharing some sort of common attributes is applied in various fields such as biology, medicine, business and computer science. A document search engine for instance, takes advantage of the information obtained clustering the document database to return a result with relevant information to the query. Two main factors that make clustering a challenging task are the size of the dataset and the dimensionality of the objects to cluster. Sometimes the character of the object makes it difficult identify its attributes. This is the case of the image clustering. A common approach is comparing two images using their visual features like the colors or shapes they contain. However, sometimes they come along with textual information claiming to be sufficiently descriptive of the content (e.g. tags on web images).
The purpose of this thesis work is to propose a text-based image clustering algorithm through the combined application of two techniques namely Minhash Locality Sensitive Hashing (MinHash LSH) and Frequent itemset Mining.
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