Application of Self-Organizing Maps in Text Clustering: A Review <s> Two Clustering strategies in Text Clustering: whole clustering and incremental clustering <s> In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model. <s> BIB001 </s> Application of Self-Organizing Maps in Text Clustering: A Review <s> Two Clustering strategies in Text Clustering: whole clustering and incremental clustering <s> Abstract Users of Web search engines are often forced to sift through the long ordered list of document `snippets' returned by the engines. The IR community has explored document clustering as an alternative method of organizing retrieval results, but clustering has yet to be deployed on most major search engines. The NorthernLight search engine organizes its output into `custom folders' based on pre-computed document labels, but does not reveal how the folders are generated or how well they correspond to users' interests. In this paper, we introduce Grouper, an interface to the results of the HuskySearch meta-search engine, which dynamically groups the search results into clusters labeled by phrases extracted from the snippets. In addition, we report on the first empirical comparison of user Web search behavior on a standard ranked-list presentation versus a clustered presentation. By analyzing HuskySearch logs, we are able to demonstrate substantial differences in the number of documents followed, and in the amount of time and effort expended by users accessing search results through these two interfaces. <s> BIB002 </s> Application of Self-Organizing Maps in Text Clustering: A Review <s> Two Clustering strategies in Text Clustering: whole clustering and incremental clustering <s> When using granular computing for problem solving, one can focus on a specific level of understanding without looking at unwanted details of subsequent (more precise) levels. We present a granular computing framework for growing hierarchical self-organizing maps. This approach is ideal since the maps are arranged in a hierarchical manner and each is a complete abstraction of a pattern within data. The framework allows us to precisely define the connections between map levels. Formulating a neuron as a granule, the actions of granule construction and decomposition correspond to the growth and absorption of neurons in the previous model. In addition, we investigate the effects of updating granules with new information on both coarser and finer granules that have a derived relationship. Called bidirectional update propagation, the method ensures pattern consistency among data abstractions. An algorithm for the construction, decomposition, and updating of the granule-based self-organizing map is introduced. With examples, we demonstrate the effectiveness of this framework for abstracting patterns on many levels. <s> BIB003
There are two common Clustering strategies, and both need to measure the similarity of the document. The first strategy is the "complete" strategy, or called "static" strategy. During the clustering process, the documents collection did not change neither adding documents, nor removing documents. At the beginning of clustering, the documents in the collection are fixed. In the clustering Method based on this policy, an N*N similarity matrix can be generated from the beginning and there are N (N − 1) / 2 similarity values in the matrix. As it will compare the similarity among any documents, the computation is very costly. The second strategy is the strategy of "incremental" BIB002 . In many occasions, the document collection can be increased at any time in the clustering process. When adding a document, it will be merged into the existing cluster, or you can separate it as a new category. While increasing documents, it may be necessary to perform re-clustering. There are some methods to calculate the similarity or distances between different clusters: 1) the shortest distance method (single link method). If G p , G q are two different clusters, Mean Quantization Error (abbreviated as MQE) is adopted as convergence condition as performed by Ref.   BIB001 . Since MQE can measure the average agglomeration degree of clustering results, when its value is less than a threshold such as 0.01 (which is adopted by Kohonen in Ref. BIB003 ), this dynamic algorithm stops. Where, C represents the quantity of clusters. Nj represents one neuron. Cj represents the cluster, which includes the data that are more similar to Nj than to other neurons. |Cj| represents the quantity of the data included by Cj. Di represents one datum among Cj.