Result for 0B2157B8F9EEFF160C0E7B458F528B2BA7519752

Query result

Key Value
FileName./usr/share/applications/weka.desktop
FileSize401
MD59EAADDB20F672E3320D079FBBF0CDC83
SHA-10B2157B8F9EEFF160C0E7B458F528B2BA7519752
SHA-256B678CE98FEB7B70DAA08F6A9AC3C59CB23D6D8C82A4BEF5E7AB51E4481412506
SSDEEP12:rqMJLMpdxSuwQ4UuoR7JYq1BsDh2Kj9t0uLV:rqMJIpdgw4UlaQsV2elV
TLSHT1B0E061C5175156B5C347D914DE06EFFE9F3A5706C9805444CCC6501D52449C985E3D6D
hashlookup:parent-total3
hashlookup:trust65

Network graph view

Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD557916F9F0F22747C82146D04D3A07337
PackageArchnoarch
PackageDescriptionWeka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
PackageMaintainerFedora Project
PackageNameweka
PackageRelease1.fc18
PackageVersion3.6.8
SHA-193C62B0C55ECDD9C1FCA96FE2363B7178A1CAD60
SHA-256696C09804D21839E9985A09D6B930AD401C63C9FF87C363B4EA6EA1630992F0E
Key Value
MD53E729A70F089D3A2D172340F3C5596CE
PackageArchnoarch
PackageDescriptionWeka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
PackageMaintainerFedora Project
PackageNameweka
PackageRelease1.fc18
PackageVersion3.6.8
SHA-138CE31307DA5279F71EEB195D526690BA3B04754
SHA-25688FD166B8C2DDA537315061B89D2E28612147FB37D805110A0F6F829CFC1069C
Key Value
MD5FB0FDC148881D2057ED6FD3B3B86138D
PackageArchnoarch
PackageDescriptionWeka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
PackageMaintainerFedora Project
PackageNameweka
PackageRelease1.fc18
PackageVersion3.6.8
SHA-10B8CB68B514D3C4025099E0DB6A462AAED99D026
SHA-2563D35131B632B13C0EEE5EE12D6A525DE6C4F90FEF6ED1F3839056C2AA131FB6A