Classification as an Approach To Requirements Analysis

James D. Palmer, Yiquing Liang, Lillian Want


Oassification schemes have proven immensely useful in computerized systems for problem solving in the physical and biological sciences. A typical example is found in the medical domain where various taxonomies have been created for diseases, symptOms, laboratory tests, drugs, and so forth. These taxonomies may be used for characterizing specific clinical situations in expert systems that assist physicians and other health professionals in diagnosis and treatment. Oassification can be divided into several phases. The activities in the first phase are to find a category structure which can fit observations. This phase is called "cluster analysis", or "typology", "learning", "clumping", "regionalization", etc., or "classification (construction)" in our paper, depending on the field to which it is applied. There are many approaches to this cluster analysis. These include approaches such as numerical taxonomy and conceptual clustering. Once this category structure has been established, the next phase is to classify new observations, that is, recognize them as members of one category or another. There are two different situations for the activities in this phase: 1) when the category structure is completely known, this kind of activity is called "classification", "indexing", or "classifying" in this paper, and 2) if category structure is partly known or only part of the information of the observation is known, this kind of activity is called "discriminant analysis" [AND73] [GOR81]. Oancey [CLA84] has characterized classification problem solving as making a selection from a set of pre-enumerated solutions (in contrast to constructing new solutions). If the problem solver has a priori knowledge of existing solutions and is able to relate these to the problem description by data abstraction and refinement, then the problem can be solved using classification. Other artificial intelligence researchers, especially those investigating machine learning, have developed new techniques such as conceptual clustering [MIC86] (in contrast to numerical/statistical clustering), which might be used for developing classification schemes for problem solving.

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