{'fraction_non_alphanumeric': 0.009874326750448833, 'fraction_numerical': 0.0008976660682226212, 'mean_word_length': 5.092896174863388, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Implementation of pattern discovery to retrieve relevant document using text mining

Many data mining techniques are used to extract the patterns from the text documents. But the challenge is using those updated patterns is still a open research issue. Most of the text mining methods generally uses the term based approaches. The main problems faced by the term based approaches is of polysemy and synonymy. This paper focuses on the implementation of an particular discovery way to discover the pattern as well as use them to retrieve the relevant document. Two important process are used i.e pattern deploying and pattern evolving is included as the pattern discovery techniques. These techniques finds to be helpful in improving the effectiveness of the discovered patterns. These discovered patterns can be used in searching the relevant and important information for text mining. To improve the effectiveness of using and updating discovered patterns according to users view, experiments on RCV1 data collection achieves user required data from a large document by removing noisy pattern by pattern evolving.