Result for 00661DE503C9641DA40E273FBA8A3B3908D9BE66

Query result

Key Value
FileName./usr/share/doc/weka-doc/doc/weka/gui/visualize/plugins/GraphVisualizePlugin.html
FileSize11313
MD5B40AB3567B90370C94C2D10349853B84
SHA-100661DE503C9641DA40E273FBA8A3B3908D9BE66
SHA-25639FDACC2CAF0E4A0C42D1DDE79393BE173FAEE714796971E8D11D01B70FCD235
SSDEEP192:HxMidVSmPgZGFicibYg/JNwpbXD1x1h850SZyl9EEXSzLP4Z8vZicibYg/I:RvPbFiciUOJibXD1x1R94P/vZiciUOI
TLSHT1DF328422185E793302AA02DC59BC1BE13BD34975E2795DC8B7FCC63D17C5FC4AA0690A
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize4773034
MD5A80C6D391FCD9DA3F5C470090E3BFB10
PackageDescriptiondocumentation for the Weka machine learning suite Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes. . Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets. . This package contains the documentation.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNameweka-doc
PackageSectiondoc
PackageVersion3.6.14-1
SHA-105AD641B6678E7C3013A36E6A0D270C660496875
SHA-256D2CD69B7451710481D83C5DBAE746A4ECDDAECF0F3B879ED7763DBE0885875BA
Key Value
FileSize4773760
MD5F762C2285A8EFEFCDBE7B3B2E731050A
PackageDescriptiondocumentation for the Weka machine learning suite Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes. . Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets. . This package contains the documentation.
PackageMaintainerDebian Java Maintainers <pkg-java-maintainers@lists.alioth.debian.org>
PackageNameweka-doc
PackageSectiondoc
PackageVersion3.6.14-1
SHA-1035BB36EF2ADD95137024A6E4092B2A77D6090FB
SHA-25653B621118EF773E031264740267EC072418EF8CB90D9D554DA90BEA6948D895E