Similarity measures matching-based

Before delving into the specific similarity calculation, we start our discussion with the characteristics of attributes in multidimensional data objects. The attributes can be quantitative or qualitative, continuous or binary, nominal or ordinal, which determines the corresponding similarity calculation (Xu and Wunsch, 2005). Typically, distance-based similarity measures are used to measure continuous features, while matching-based similarity measures are more suitable for categorical variables. 

Matching-Based Similarity Measures For categorical attributes, distance-based similarity measures cannot be performed directly. The most straightforward way is to compare the similarity of objects for pairs of categorical attributes (Zhao and Karypis, 2005). For two objects that contain simple binary attributes, ... 

Rarey M, Dixon JS. Feature trees a new molecular similarity measure based on tree matching. / Comput Aided Mol Des 1998 12 471-90. 

Fourier transform spectroscopy technology is widely used in infrared spectroscopy. A spectrum that formerly required 15 min to obtain on a continuous wave instrument can be obtained in a few seconds on an FT-IR. This greatly increases research and analytical productivity. In addition to increased productivity, the FT-IR instrument can use a concept called Fleggetts Advantage where the entire spectrum is determined in the same time it takes a continuous wave (CW) device to measure a small fraction of the spectrum. Therefore many spectra can be obtained in the same time as one CW spectrum. If these spectra are summed, the signal-to-noise ratio, S/N can be greatly increased. Finally, because of the inherent computer-based nature of the FT-IR system, databases of infrared spectra are easily searched for matching or similar compounds. 

Rarey, M. and Dixon, J.S. (1998). Feature Trees A New Molecular Similarity Measure Based on Tlee Matching. J.Comput.Aid.Molec.Des., 12,471-490. 

Given a user defined K or a threshold on the minimum certainty, the system can produce alternative matchings and assign a probability estimate of correctness to each of them. The probability is based on the similarity measure, as assigned by an ensemble of matchers. To justify this method, we use the monotonicity principle, as discussed before. 

In this section, we have mainly presented user inputs, i.e., optional preferences and parameters applied to data. To sum up, the quality can be improved by using external resources and expert feedback. Several tools are based on machine learning techniques either as a similarity measure (mostly at the instance level) or as a means of combining the results of similarity measures. In both cases, training data is a crucial issue. Finally, many tools propose preferences or options which add more flexibility or may improve the matching quality. The next section focuses on the parameters at the similarity measure level. 

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