Text Mining: Concepts, Implementation, and Big Data ChallengeSpringer, 7 Jun 2018 - 373 halaman This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management. |
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Text Mining: Concepts, Implementation, and Big Data Challenge Taeho Jo Pratinjau tidak tersedia - 2018 |
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approaches Apriori algorithm association rules big data binary classification chapter clustering algorithm clustering data items clustering index clustering results computed by Eq corpus cosine similarity data clustering data mining defined describe Document domain encoded into numerical encoding texts extracted F1 measure full text fuzzy clustering illustrated in Fig initial input vector interface intra-cluster similarity item sets Kohonen Networks Let us consider Let us mention list of words machine learning algorithms mail filtering matrix mean vectors mentioned in Sect method nearest neighbors novice texts number of clusters numerical vectors output paragraph pairs predefined categories relational data mining retrieval sample texts scheme section is concerned selected shown in Fig subtexts supervised learning support vector machine taxonomy text association text categorization system text classification text clustering system text collection text indexing text mining tasks text segmentation text summarization texts into numerical TF-IDF training examples unsupervised updated values web mining whereas