21.23 Identifying Frequent Items and Associations
One thing we often to first do is to get an idea of the most frequent terms in the corpus. We use tm::findFreqTerms() to do this. Here we limit the output to those terms that occur at least 1,000 times:
## [1] "data" "mine" "use"So that only lists a few. We can get more of them by reducing the threshold:
## [1] "accuraci" "acsi" "advers" "algorithm" "also"
## [6] "approach" "associ" "australia" "australian" "build"
## [11] "call" "classif" "common" "comput" "consist"
## [16] "csiro" "data" "databas" "dataset" "develop"
## [21] "discoveri" "effici" "explor" "graham" "howev"
## [26] "implement" "import" "inform" "kdd" "knowledg"
## [31] "larg" "may" "mine" "network" "neural"
## [36] "page" "particular" "perform" "problem" "provid"
## [41] "requir" "research" "rule" "scienc" "search"
## [46] "signific" "structur" "system" "techniqu" "technolog"
## [51] "time" "tool" "train" "univers" "use"
## [56] "weight" "william" "within" "work" "allow"
## [61] "analysi" "appli" "applic" "area" "attribut"
## [66] "base" "can" "case" "chang" "class"
## [71] "classifi" "cluster" "collect" "combin" "compar"
## [76] "condit" "confer" "contain" "decis" "defin"
## [81] "describ" "differ" "discov" "discuss" "distanc"
## [86] "distribut" "domain" "estim" "event" "exampl"
## [91] "exist" "featur" "find" "first" "follow"
## [96] "form" "function" "general" "generat" "given"
## [101] "group" "hybrid" "identifi" "includ" "increas"
## [106] "interest" "intern" "interv" "lead" "learn"
## [111] "level" "like" "link" "make" "mani"
## [116] "mean" "measur" "method" "model" "multipl"
## [121] "need" "new" "number" "object" "observ"
## [126] "occur" "often" "one" "paper" "pattern"
## [131] "period" "point" "predict" "present" "proceed"
## [136] "process" "propos" "record" "refer" "regress"
## [141] "relat" "report" "repres" "result" "sampl"
## [146] "section" "select" "sequenc" "set" "similar"
## [151] "singl" "stage" "state" "statist" "step"
## [156] "studi" "subset" "support" "target" "task"
## [161] "tempor" "three" "transact" "tree" "two"
## [166] "type" "understand" "user" "valu" "variabl"
## [171] "well" "will" "year" "age" "avail"
## [176] "averag" "care" "claim" "consid" "cost"
## [181] "day" "detect" "drug" "effect" "episod"
## [186] "error" "expect" "expert" "fig" "figur"
## [191] "health" "hospit" "http" "indic" "individu"
## [196] "intellig" "journal" "machin" "medic" "node"
## [201] "packag" "patient" "popul" "servic" "show"
## [206] "small" "sourc" "tabl" "test" "total"
## [211] "unexpect" "angioedema" "current" "evalu" "high"
## [216] "interesting" "order" "ratio" "reaction" "risk"
## [221] "unit" "usual" "visual" "entiti" "experi"
## [226] "hot" "insur" "map" "nugget" "open"
## [231] "polici" "size" "spot" "random" "vector"
## [236] "outlier" "pmml" "rank" "rnn" "window"
## [241] "adr" "oper" "forest" "subspac" "rattl"
## [246] "utar"We can also find associations with a word, specifying a correlation limit.
## $data
## mine induct challeng know answer need
## 0.90 0.72 0.70 0.65 0.64 0.63
## statistician general foundat major mani boost
## 0.63 0.62 0.62 0.61 0.61 0.61
## come
## 0.60If two words always appear together then the correlation would be 1.0 and if they never appear together the correlation would be 0.0. Thus the correlation is a measure of how closely associated the words are in the corpus.
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