By David Talbot, James Talbot

Constructed by way of Jean-Paul Benzérci greater than 30 years in the past, correspondence research as a framework for studying facts quick stumbled on frequent attractiveness in Europe. The topicality and significance of correspondence research proceed, and with the large computing energy now to be had and new fields of program rising, its importance is bigger than ever.

Correspondence research and knowledge Coding with Java and R basically demonstrates why this method is still vital and within the eyes of many, unsurpassed as an research framework. After providing a few ancient historical past, the writer offers a theoretical review of the maths and underlying algorithms of correspondence research and hierarchical clustering. the point of interest then shifts to facts coding, with a survey of the generally various probabilities correspondence research deals and advent of the Java software program for correspondence research, clustering, and interpretation instruments. A bankruptcy of case reports follows, in which the writer explores functions to parts comparable to form research and time-evolving info. the ultimate bankruptcy experiences the wealth of reports on text in addition to textual shape, performed by way of Benzécri and his examine lab. those discussions convey the significance of correspondence research to synthetic intelligence in addition to to stylometry and different fields.

This ebook not just indicates why correspondence research is necessary, yet with a transparent presentation replete with recommendation and assistance, additionally exhibits how you can placed this system into perform. Downloadable software program and knowledge units permit speedy, hands-on exploration of leading edge correspondence research functions.

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**Example text**

If we apply a linear mapping given by matrix M to the vector space E, in the transform space we can use this same linear mapping to deﬁne scalar product, distance, norm and orthogonality using the analogous function g: g(x, y) = x M y. To satisfy the requirements of g being symmetric, positive and deﬁnite, we require M to be a symmetric positive deﬁnite matrix. We then can deﬁne the norm ( x 2M = x M x), the Euclidean distance (dM (x, y) = x − y M ) and M -orthogonality ( x, y M = x M y = 0 if x is M -orthogonal to y).

The mass is the marginal distribution of the input data table. Let us take a step back: the given contingency table data is denoted kIJ = {kIJ (i, j) = k(i, j); i ∈ I, j ∈ J}. We have k(i) = j∈J k(i, j). Analogously k(j) is deﬁned, and k = i∈I,j∈J k(i, j). Next, fIJ = {fij = k(i, j)/k; i ∈ I, j ∈ J} ⊂ RI×J , similarly fI is deﬁned as {fi = k(i)/k; i ∈ I, j ∈ J} ⊂ RI , and fJ analogously. 3: the conditional distribution of fJ knowing i ∈ I, also termed the jth proﬁle with coordinates indexed by the elements of I, is fJi = {fji = fij /fi = (kij /k)/(ki /k); fi = 0; j ∈ J} and likewise for fIj .

Normalized factors: on the sets I and J, we next deﬁne the functions φI and ψ J of zero mean, of unit variance, pairwise uncorrelated on I (respectively J), and associated with masses fJ (respectively fI ). i∈I fi φα (i) = 0; j∈J fj ψα (j) = 0 2 2 i∈I fi φα (i) = 1; j∈J fj ψα (j) = 1 i∈I fi φα (i)φβ (i) = δαβ ; j∈J fj ψα (j)ψβ (j) = δαβ Between unnormalized and normalized factors, we have the relations: i∈I −1 φα (i) = λα 2 Fα (i) ∀i ∈ I, ∀α = 1, 2, . . N −1 ψα (j) = λα 2 Gα (j) ∀j ∈ J, ∀α = 1, 2, .