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A Python implementation of theFuzzTX (Fuzzy Temporal Crossing) algorithm. The algorithm applies a fuzzy triangular membership function to cross time-series data from different and/or unrelated sources based on thedate-time attributes.Research paper was accepted as a conference paper at the 2020 ADBIS, TPDL & EDA joint conferences:

• Owuor D.O., Laurent A., Orero J.O. (2020) Exploiting IoT Data Crossings for Gradual Pattern Mining Through Parallel Processing. In: Bellatreche L. et al. (eds) ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium. TPDL 2020, ADBIS 2020. Communications in Computer and Information Science, vol 1260. Springer, Cham.https://doi.org/10.1007/978-3-030-55814-7_9

You will be required to install the following python dependencies before usingFuzzTX algorithm:

                   install python (version => 3.6)

                    $ pip3 install numpy python-dateutil scikit-fuzzy

Use it a command line program with the local package:

$python3 src/init_fuzztx_csv.py -a allowChar -f'file1.csv,file2.csv,file3.csv'

where you specify the input parameters as follows:

• files.csv - [required] files in csv format separated by commas

• allowChar - [optional] allow charactersdefault = 0. If set to 1, the algorithm will cross all columns including those that have non-numeric values.

For example we executed theFuzzTX algorithm on sample data-sets

$python3 src/init_fuzztx_csv.py -a 0 -f'../data/oreme/GPS.csv,../data/oreme/Omnidir.csv'

The output should be a generated csv file(x_data.csv). For purposes of demonstration, we display the contents (as a nested array) below

[    ['timestamp','id_site','v1','v2','id_site','Rx','Hmax','Thmax','H1/3','Th1/3','Hmoy','Tmoy','Cambrure','Nb_Vagues'],     ['2012-01-01 00:30:00','8','49','67','1','100.0000000000','1.4900000000','5.1700000000','0.8600000000','4.5700000000','0.5400000000','3.8000000000','5.7000000000','315'],     ['2012-01-01 01:29:58','8','52','67','1','100.0000000000','1.9500000000','5.6600000000','1.1500000000','4.8600000000','0.7500000000','4.2300000000','6.1000000000','282']]0.0747671127319336seconds

• MIT

• Dickson Owuor, Anne Laurent, and Joseph Orero (2019). Mining Fuzzy-temporal Gradual Patterns. In the proceedings of the 2019 IEEE International Conference on Fuzzy Systems (FuzzIEEE). IEEE.https://doi.org/10.1109/FUZZ-IEEE.2019.8858883.