Middle and Late Bronze Age Metal Tools from the Aegean, Eastern ...

hoards from Crete, each dated to the LM I period (#6, 7 from Mochlos and 10 from
Knossos); these assemblages are noted for their diverse range of tool types. The second
sub-cluster has nine hoards chiefly from the mainland except for one Cypriot example
(#32, the Enkomi Foundry hoard). This tight cluster is extremely significant as these
mainland hoards (16: Mylonas; 18: Tsountas; 20: Athens; 17: Poros Wall; 23: Thebes;
26: Kalydon; 22: Anthedon; 24: Orchomenos) were found relatively close to each other
and dated to the LH IIIB or C period.
It is possible to compare the coefficient similarities of Cluster 3 hoards by tool
variables. In other words, the hoard data from this grouping can be clustered by
“variable” (tool types) instead of by “case” (hoards). 626
A similarity chart, using the
Jaccard’s Coefficient was thus created for the tool variables of Cluster 3 (Fig. 5.9). These
similarities were then plotted on a dendrogram using average linkage (between groups)
cluster analysis (Fig. 5.11). There are several relationships among tool types that are
readily discernible from this statistical distribution. Within the metal assemblages in
Cluster 3, double axes and broad chisels show the strongest link for being hoarded
together. Knives or razors have a strong correlation with agricultural tools, and both
implement types are subsequently linked with narrow chisels. The set of knives/razors,
agricultural tools, and narrow chisels are then clustered with double axes and broad
chisels. Collectively, these five implements form a distinct grouping, and this array of
implements may indicate a typical tool set. The data from these hoards show that
combination tools and smith implements also cluster together, as do saws, single/flat
626 Note that the similarity coefficient for all 54 hoards was based upon comparing each “case” or hoard. In
this instance, it the “variable” or tool type that is the basis for the creation of a similarity matrix. For a
discussion on clustering by variables instead of cases, see Drennan 2009, 316-320.
265