document: Automatic record linkage using seeded nearest neighbor and support vector machine classification The task of linking databases is an important step in an increasing number of data mining projects , because linked data can contain information that is not available otherwise , or that would require time-consuming and expensive collection of specific data . The aim of linking is to match and aggregate all records that refer to the same entity . One of the major challenges when linking large databases is the efficient and accurate classification of record pairs into matches and non-matches . While traditionally classification was based on manually-set thresholds or on statistical procedures , many of the more recently developed classification methods are based on supervised learning techniques . They therefore require training data , which is often not available in real world situations or has to be prepared manually , an expensive , cumbersome and time-consuming process . The author has previously presented a novel two-step approach to automatic record pair classification ( 6 , 7 ) . In the first step of this approach , training examples of high quality are automatically selected from the compared record pairs , and used in the second step to train a support vector machine ( SVM ) classifier . Initial experiments showed the feasibility of the approach , achieving results that outperformed k-means clustering . In this paper , two variations of this approach are presented . The first is based on a nearest-neighbour classifier , while the second improves a SVM classifier by iteratively adding more examples into the training sets . Experimental results show that this two-step approach can achieve better classification results than other unsupervised approaches .
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