| Key | Value |
|---|---|
| FileName | ./usr/bin/weka |
| FileSize | 116 |
| MD5 | A11E79F973A2E95C4AB64CEF16AAAB4A |
| SHA-1 | 1FA781AD8F97E8FD22651F6FA19F366DC3DE046C |
| SHA-256 | F76033A00AB62655FD5374BEFB75AC7094CDA2EDFB99D559366EDFA595B6824F |
| SSDEEP | 3:TKH/xLWGv8TGI4RdlH+vQjUBEzErFHzr7+PX4:WoaIcCvexzAt2I |
| TLSH | T17DB092F5B1002525E92E4674BA8B28D43690A4F49B04E99774D011C92FA2B4500E6A12 |
| hashlookup:parent-total | 2 |
| hashlookup:trust | 60 |
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 |
|---|---|
| MD5 | AC061703A460E873D11A166676578EA0 |
| PackageArch | noarch |
| PackageDescription | Weka 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. |
| PackageMaintainer | Fedora Project |
| PackageName | weka |
| PackageRelease | 3.fc11 |
| PackageVersion | 3.6.0 |
| SHA-1 | 4C53147CAF0564A176B1EA678D6555E8FB02BEE4 |
| SHA-256 | 6B0197661494866799DA8F0516EBC13207C9B9549C0FECDF3BEA882963B174EA |
| Key | Value |
|---|---|
| MD5 | A1904AF3DDF485ACCA318BAB2FEB70DB |
| PackageArch | noarch |
| PackageDescription | Weka 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. |
| PackageMaintainer | Fedora Project |
| PackageName | weka |
| PackageRelease | 4.fc12 |
| PackageVersion | 3.6.0 |
| SHA-1 | 9E36553C85D899DB44931A39F61F817BAD9F44D5 |
| SHA-256 | C6806DD8B7140A3E6A8E0DA308178B1BD14B570F09305B8CDA27670430CC30AB |