Preprocessing. data set

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  • Data warehouses usually have some missing values due to unavailable data that affect the number and the quality of the generated rules. The missing values could affect the coverage percentage and number of reduces generated from a specific data set. Missing values lead to the difficulty of extracting useful information from data set. Association rule algorithms typically only identify patterns that occur in the original form throughout the database.

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  • For each extracted TCP connection, we record the sequence of size, arrival time tuples for each packet in the connection, in arrival order. We encode the packet’s direction in the sign bit of the packet’s size, so that packets sent from server to client have size less than zero and those from client to server have size greater than zero. Since the traces in this data set consist mostly of unencrypted, non-tunneled TCP connections, a few additional preprocessing steps are necessary to simulate the more challenging scenarios which our techniques are designed to address.

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  • Sequential Method ( Preprocessing Methods) Parrarel Method missing value is taking into account in main process knowledge mining.Do Not Impute ( parrarel method) Case Deletion / Row Ignoring. Fill in the missing value / Imputation Method.The rising of new pattern because of wrong imputation method Biasing natural pattern in original data.Given a set of objects, the overall objective of clustering is to divide the data set into groups based on similarity of objects, and to minimize the intra-cluster dissimilarity.

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  • Dividing sentences in chunks of words is a useful preprocessing step for parsing, information extraction and information retrieval. (l~mshaw and Marcus, 1995) have introduced a "convenient" data representation for chunking by converting it to a tagging task. In this paper we will examine seven different data representations for the problem of recognizing noun phrase chunks. We will show that the the data representation choice has a minor influence on chunking performance.

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  • We propose a language-independent method for the automatic extraction of transliteration pairs from parallel corpora. In contrast to previous work, our method uses no form of supervision, and does not require linguistically informed preprocessing. We conduct experiments on data sets from the NEWS 2010 shared task on transliteration mining and achieve an F-measure of up to 92%, outperforming most of the semi-supervised systems that were submitted.

    pdf10p hongdo_1 12-04-2013 14 1   Download


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