When used in conjunection with the Eclipse SVN plugin it enables version control and effective design brainstorming. I found WireframeSketcher a must have addin for Eclipse, enabling me to complete very detailed wireframes for clients within the development ennvironment. I've just written article about my experiences of using WireframeSketcher for the last month. Posted in Tools | 2 Comments » 2 Responses to “WireframeSketcher 1.4.2” Overall, this one is a very interesting tool. What is also interesting is that in this way, users can also combine multiple master pages, providing even more flexibility. Instead, pages are simply created and if a user wishes to reuse a page as a master, the page is inserted as a “Master Screen” object anywhere on a new page. With WireframeSketcher users do not have to worry about defining masters up front (at the time when it still it is not known which elements are to be shared across). Another unique feature of this program is how it handles masters. This features is an interesting exploration of the communicative role of prototyping which many UI tools fall short on. Basically, drawn screens can be organized in a linear fashion into presentable stories. What sets WireframeSketcher apart is definitely the storyboard mode (as seen on the second screen). The latest version also supports states for certain form elements allowing to specify such things as disabled, selected or enabled modes. This tool comes with a wide palette of draggable user interface elements, widgets, sliders, and icons as most user interface designers would expect. The graph in Figure 9 is the Tile model derived from the example window shown in Figure 1.WireframeSketcher is a plugin for the popular Eclipse development platform and allows for the creation of quick wireframes. Once this entire process has been accomplished, we iterate over the Relations and for each one we use PartitionMap to discover which closeness level must be assigned to the Relation by comparing the distance with the ranges. In the example, PartitionMap maps each range to a closeness level: ( −∞, 14] = 1 and [15, + ∞ ) = 2. For each cluster, we obtain the minimum and a maximum value in pixels (lines 27). The lesser the values (distances) of the cluster, the lesser the numerical value of the tag (the lower distance group is tagged with 1 ). After the clusters have been obtained, they are sorted and a numerical tag is assigned to each one (lines 23 to 30). To continue with the example, the k-means algorithm is applied with nClusters = 2 and the output is: BestPartition = 2 < maxDev, so the clustering loop stops. 10 geneity criterion, which is the sum of the squared errors (line 13). As mentioned in Section 4.1.2, the comparisons of the positions take into account a certain amount of margin, which is the reason why the xAllenInterval of the relation between nameField and passwordField is EQUALS although the projection of the coordinates in the X axis is not exactly the same for both widgets. This unique group is assigned the closeness level of 1. The number of iterations, Num Iterations variable, is by default 20, and we keep the best solution according to the intra-cluster homo- Since all the distances between the nodes are more or less similar, the clustering algorithm groups all the distances in just one cluster. In order to obtain a better clustering, the algorithm is executed multiple times (lines 11 to 16) with di ff erent random starting conditions. Because k-means is a heuristic algorithm, it is very fast, but it could fall into a local maximum. This condition is that the standard deviation of every cluster is less than maxDev (line 17). We therefore apply the k-means algorithm several times (lines 6 to 20), while increasing the number of clusters in each iteration (line 10) until the stop condition. However, we do not know the number of clusters a priori. Given that k-means is a divisive algorithm, the number of clusters must be passed as a parameter. In order to perform the clustering of distances, we have selected the k-means algorithm (line 12), with the euclidean distance as a similarity function.
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