利用元胞自动机提高信息检索聚类质量
Improving Quality of Clustering using Cellular Automata for Information retrieval
摘要：
聚类由于其潜在的改进倒排文件搜索的效率而被广泛应用于信息检索中。聚类是一个无监督的过程，大多数聚类算法依赖于某些假设来定义数据集中存在的子组。聚类质量度量是一个函数，给定数据集及其聚类划分，返回一个非负实数，表示该聚类的质量。此外，它们可以根据数据集的特征及其输入参数值以不同的方式表现。因此，在大多数应用程序中，所得到的聚类方案需要某种评估其有效性。利用元胞自动机分类器进行信息检索，可以提高聚类的质量。在这项研究中，我们认为，如果细胞自动机与聚类应用于搜索结果（查询特定的聚类），那么它有可能增加检索效率相比，静态聚类和传统的倒排文件搜索。我们进行了一些实验，使用十个文档集合和八个层次聚类方法。我们的研究结果表明，查询特定的聚类与元胞自动机的有效性确实较高，并建议有其应用范围IR。
英文摘要：
Clustering has been widely applied to Information Retrieval (IR) on the grounds of its potential improved effectiveness over inverted file search. Clustering is a mostly unsupervised procedure and the majority of the clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set .A clustering quality measure is a function that, given a data set and its partition into clusters, returns a non-negative real number representing the quality of that clustering. Moreover, they may behave in a different way depending on the features of the data set and their input parameters values. Therefore, in most applications the resulting clustering scheme requires some sort of evaluation as regards its validity. The quality of clustering can be enhanced by using a Cellular Automata Classifier for information retrieval. In this study we take the view that if cellular automata with clustering is applied to search results (query-specific clustering), then it has the potential to increase the retrieval effectiveness compared both to that of static clustering and of conventional inverted file search. We conducted a number of experiments using ten document collections and eight hierarchic clustering methods. Our results show that the effectiveness of query-specific clustering with cellular automata is indeed higher and suggest that there is scope for its application to IR.
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