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1234 G 1 1 3 Exp 1 2 3 4 G So as not to duplicate axes, this copy of G should be folded over to coincide with the other copy, producing a "conical" unipartite.

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Presentation on theme: "1234 G 1 1 3 Exp 1 2 3 4 G So as not to duplicate axes, this copy of G should be folded over to coincide with the other copy, producing a "conical" unipartite."— Presentation transcript:

1 1234 G 1 1 3 Exp 1 2 3 4 G So as not to duplicate axes, this copy of G should be folded over to coincide with the other copy, producing a "conical" unipartite card. The Bipartite, Unipartite-on-Part Experiment Gene Relationship, EGG

2  Customer 1 2 3 4 Item 7 6 5 4 3 2 t 1 6 5 4 3 Gene 1 1 1 Doc 1 2 3 4 Gene 1 1 3 Exp 1 1 1 1 1 1 1 1 1234 Author 1234 G 56 term  7 567 People  1 1 1 1 1 1 3 2 1 Doc 2345 PI People  cust item card authordoc card termdoc card docdoc card (hyperlink anal.) termterm card (share stem?) expgene card gene gene card (ppi) expPI card Each axis, a, inherits a frequency attribute from each of its cards, c(a,b), denoted bf(c.a)  "# of b s related to a" (e.g., df(t) = doc freq of term, t). Of course, bf(c.a) is inherited redundantly by c(a,b). Each card, c(a,b), inherits a frequency attribute from each of its axes, a [b], denoted af(a,b)  "# times a is related to b in c" [bf(a,b)  "# times b~a in c"] Each card, c(a,b), can be expanded by each of its axes, e.g., a, to a-sets (each a value is identified with the singleton, {a}) (e.g., itemsets in MBR) or a-sets can become a new axis (e.g., doc in IR. Note, if term is expanded by singleton termsets to be part of doc, then the termdoc card becomes a cone (see first slide)). Next we put some of the descriptive attributse in their places. Note: Conf / non-conf rules partition itemset-itemset card. Can we usefully list confident rules by specifying the boundary (SVM style)? That presuppose spatial continuity of conf rules (may not be correct assumption) but it may be on another similar card? 5 6 16 ItemSet Supp(A) = CusFreq(ItemSet) gene gene card (ppi) DataDex Model ItemSet antecedent 12345616 itemset itemset card Conf(A  B) =Supp(A  B)/Supp(A)

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4  Customer 1 2 3 4 Item 1 2 3 4 Gene 1 1 3 Exp 1 1 1 1 1 1 1 1 1234 Author 1234567 567 People  cust itemset card author doc expgene card gene gene card (ppi) expPI card 5 6 16 ItemSet DataDex Model combining term  doc and item  itemset (no animation) ItemSet ( antecedent) 12345616 itemset itemset card 89512 doc  term  gene  PI termterm termdoc 1 1 1 1 3 1 1 3 1 1 1 ItemSet can be replaced by ItemBag (allowing duplicates and promoting count analysis).

5  Customer 1 2 3 4 7 6 5 4 3 2 t 1 6 5 4 3 Gene 1 1 1 Doc 1 2 3 4 Gene 1 1 3 Exp 1 1 1 1 1 1 1 1 1234 Author 1234 G 56 term  7 567 People  1 1 1 1 1 1 3 2 1 Doc 2345 PI People  cust itembag card authordoc card termdoc card docdoc card termterm card (share stem?) expgene card gene gene card (ppi) expPI card ItemBag gene gene card (ppi) DataDex uncombining term-doc and item-itemset (using itembag (basket) so item count in a basket is defined. ItemBag 1234 Item 5 6 ∞ 56∞ itembag itembag card What is term frequency? doc frequency? 1. TD is a bag-edged graph, i.e., Edge(TD) is a bag, meaning an edge can occur multiple times (the same term "can occur in" a doc many times). If we don't distinguish those occurrences other than existence (could distinguish them into type classes, e.g., verb, noun... ) then TD can be realized as a set-edged graph with a count label, otherwise we must use a bag-edged graph with a type label. Usually, TD is the former and the count label is term frequency. Document frequency is a Term node label which is is the node degree (# of docs to which it relates). A market basket is also a bag-edged graph which is realized as a set-edged graph with a count label.

6 3 4 5 6 gene gene card  Customer 1 2 3 4 Item 7 6 5 4 3 2 1 -90 :. 90 1 1 1 Doc Gene 1 1 3 Exp 1 1 1 1 1 1 1 1 1234 Author 1234 G 56 term  7 567 People  1 1 1 1 1 1 3 2 1 Doc 2345 PI People  authdoc card termdoc card docdoc card termterm card expgene card expPI card 5 6 16 ItemSet DataDex Model 12345616 itemset itemset card cust item set card exp loc card Loc axis / card Lat axis Lon axis 0..360 RSI card

7 3 4 5 6 gene gene card  Customer 1 2 3 4 Item 7 6 5 4 3 2 1 1 1 1 Doc Gene 1 1 3 Exp 1 1 1 1 1 1 1 1 1234 Author 1234 G 56 term  7 567 People  1 1 1 1 1 1 3 2 1 Doc 2345 PI People  authdoc card termdoc card docdoc card termterm card expgene card exp PI 5 6 ∞ ItemBag DataDex Model 123456∞ itembag itembag card cust item bag card exp loc card Loc (Lat-Lon) 1 2 3 4 5 6 Time RSI video RSI card 1 1 1 Grnd Image card (loc=camera loc) Aperture angle axis Grnd Video card

8 1 1 1 1 Exp 1 1 1 1 1 1 1 1 term  gene People  Author|Cust People  PI Cust Itembag AuthDoc TermTerm (GeneGene) ExpGene Exp PI ItemBag Doc Term Doc Doc ItembagItembag Loc Exp Loc LocIntensity (Band) Intensity

9 1 1 1 1 Exp 1 1 1 1 1 1 1 1 term  gene People  Author|Cust People  PI Cust Itembag AuthDoc TermTerm (GeneGene) ExpGene Exp PI ItemBag Doc Term Doc Doc ItembagItembag Lat ExpLoc (genes from specimen in lat) Band (multispectral multitemporal) Lon


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