Hi! The formula I used was Definition 1. Definition 2 is incorrect, isn't it? (Or at least "elusion" and "prevalence" are being used loosely to mean "discard yield" and "collection yield".)

William

Definition 1. In his earlier work (cited above) he uses prevalence to estimate TP+FN (the total number of relevant documents) and he uses elusion to estimate FN (the total number of missed relevant documents. He then plugs these estimates into a contingency table with N (the total number of documents) and D (the total number of discarded documents). If I am not mistaken, the resulting formula is

eRecall = 1 - (elusion/prevalence * D/N)

Definition 2. In his most recent work (http://orcatec.com/2014/11/04/the-pendulum-swings-practical-measurement-in-ediscovery/), he defines

eRecall = 1 - elusion/prevalence

These clusters are reviewed by the review team with the largest clusters being reviewed first. Clusters that are clearly irrelevant can be discarded based on a knowledge of what is in the cluster, and documents in clusters that are clearly relevant can all be deemed relevant. Clusters that may contain some but not all relevant documents can be passed on to the review platform where like documents can be batched to the same reviewers speeding review and helping ensure consistency

The review process can begin as soon as documents have been collected and the clustering has begun. Relevance decisions are persistent and once made are applied to documents that are later added to the cluster.

BeyondRecognition also has "Find" technology that provides search capability with operators based on absolute page coordinates (i.e., within certain range of coordinates on a page) or on relative page coordinates (i.e., one range of coordinates with a specified position relative to another) as a way to further refine decision-making. Visual classification technology also has advantages beyond culling, e.g., redactions can be made based on the page locations of text values, or can be done zonally within clusters to redact even handwriting.

If visual classification has been implemented as a core information governance tool, the organization may also have applied document-type labels to the clusters as a way to assist in applying retention schedules, setting user access permissions, detecting PII, and determining where the content should be stored, and those labels can be used when first gathering or collecting documents. For example, the various clusters that have been labelled as "invoices" will often not be remotely relevant to most litigation and need not be collected.

Tying selection to ongoing information governance minimizes the ongoing e-discovery problem of continually reinventing the wheel. For example, if the files or documents of someone who is routinely provided copies of invoices are included in multiple litigation discovery collections, how many times does the company have to pay to have all those invoices processed and evaluated to determine that they are not relevant?

You and John have commented in practically every edisco blog that your "visual clustering" tool will effectively find relevant in textual and non-textual documents alike.

It is not clear to me from reading your marketing material what your process is or why it would work. I understand that you identify "glyphs" and do clustering, but that's not enough to code my documents.

In contrast, I believe I understand the role of Catalyst's diversity sampling and Relativity's "stratified" sampling -- both are methods of identifying training documents for a learning algorithm. Perhaps yours is more similar to Relativity's, in which you select members of each cluster for training? Or do you manually review each cluster?

Can you point me to a study that elaborates your process and validates your effectiveness claim?

Gordon