CloSpan: Mining Closed Sequential PatternsAuthor: Xifeng Yan, University of Illinois at Urbana-ChampaignThe program is built upon PrefixSpan source code, "PrefixSpan:Mining Sequential Patterns Efficiently by Prefix-Projected PatternGrowth" Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Helen Pinto,Qiming Chen, Umeshwar Dayal, Mei-Chun Hsu (ICDE'2001)Contact: xyan@cs.uiuc.eduReference: "X. Yan, J. Han, R. Afshar, CloSpan: Mining ClosedSequential Patterns in Large Databases, Proc. 2003 SIAM Int. Conf.Data Mining (SDM'03)", 166 - 177, 2003.NOTE:   For compiling under VC, please install stlport first.How-To:    CloSpan filename min_sup num_of_labels    Parameters: (1) filename, your binary data (2) min_sup, the    minimum frequency of patterns (3) num_of_labels, the number of    distinct item labels.    Example:        CloSpan D10N1B.data 0.1 1000        It mines all frequent sequences from "D10N1B.data", each of which        should appear in at least 10% of the sequences in the dataset. 1000        means there are 1000 different symbols in this dataset.Input Format:    1. The input is a set of sequences; each sequence has the following    format    <(item_11, item_12, ..., item_1n)(item_21, item_22, ... item_2m)...>      ------------------------------  -----------------------------           transaction 1                    transaction 2 ......    Example:        <(ab)(c)(d)>        <(e)(acfh)>        ...    The input is stored in a binary file, we use a 4-byte integer "-1" to    separate transactions in each sequence and another 4-byte integer "-2"    to separate sequences in a dataset.  Each of items is encoded using a    4-byte integer. For example, <(ab)(c)(d)><(e)(acfh)> is stored as            ab-1c-1d-1-2e-1acfh-1-2    where each symbol is a 4-byte integer and all of them are concatenated    together.Output:    Program status as it is executing and the final results (such as timing)    are printed to stdout (console).    The discovered patterns are stored in a file named "ClosedPatterns",    which is in a format of plain text.    The first column in the output file shows the discovered patterns.    The second column in the output file is the number of times that a    pattern appears in the dataset.