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如何使用agrep获得模糊字符串匹配的精确常用“max.distance”值？

[英]How can I get the precise common “max.distance” value for fuzzy string matching using agrep?

 原文  2018-09-11 10:25:58  3   2    r/ string-matching/ agrep

问题描述

I am trying to figure out the best precision for fuzzy string matching between two string names using agrep. 我试图找出使用agrep在两个字符串名称之间进行模糊字符串匹配的最佳精度。

However, I will need to choose one precision "max.distance" to apply the same across all strings I am trying to match since the amount of string is huge. 但是，我需要选择一个精度“max.distance”来在我想要匹配的所有字符串中应用相同的字符串，因为字符串的数量很大。 It won't be possible to choose the best precision value "max.distance" for every each string I am trying to match. 我不能为我想要匹配的每个字符串选择最佳精度值“max.distance”。

For instance, let's say I use the precision "max.distance" as "0.2", "0.1", and "0.05" for each "BANK OF AMERICA CORP" and "1st Capital Bank". 例如，假设每个“BANK OF AMERICA CORP”和“1st Capital Bank”使用精度“max.distance”为“0.2”，“0.1”和“0.05”。

First, the below is for "BANK OF AMERICA CORP" for "max.distance" of "0.2","0.1", and "0.05": 首先，下面是“BANK OF AMERICA CORP”的“max.distance”为“0.2”，“0.1”和“0.05”： 

    > agrep("BANK OF AMERICA CORP",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.2)
     [1] "BANK OF AMERICA/PRIVATE BANK WEST"   "BANK OF AMERICA SECURITIES"         
     [3] "BANK OF AMERICA SEC LLC"             "BANK OF AMERICA SECURITIES LLC"     
     [5] "BANK OF AMERICA NT & SA"             "BANK OF AMERICA CORP"               
     [7] "ALLIANZ OF AMERICA CORP"             "Bank of America Securities/Vice Pre"
     [9] "Bank of America Securities/Investme" "Bank of America/President"          
    [11] "Bank of America Securities LLC/Prin" "Bank of America Securities LLC/Mana"
    [13] "Bank of America Securities LLC/Inve" "Bank of America Securities/Principa"
    [15] "Bank of America Securities LLC/Bank" "Bank of America Sec/Investment Bank"
    [17] "Bank Of America Securities/Managing" "Bank of America/Chairman--Midwest A"
    [19] "Bank of America Securities LLC/Vice" "Bank of America Corporation/Sales C"
    [21] "Bank of America Securities/Broker"   "Bank of America Corporation/Banker" 
    [23] "Bank of America Corporation/Senior"  "Bank of America Securities/Equity R"
    [25] "Bank of America Corporation/Vice Ch" "BANK OF AMERICA CORPORATION"        
    [27] "BANK OF AMERICA HEADQUARTERS"        "BANK OF AMERICA ADMINISTRATION"     
    [29] "BANK OF AMERICA N A"                 "Bank of America/Commercial Banking" 
    [31] "Bank of America Sec./Investment Ban"
    > 
    > agrep("BANK OF AMERICA CORP",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.1)
    [1] "BANK OF AMERICA CORP"                "ALLIANZ OF AMERICA CORP"            
    [3] "Bank of America Corporation/Sales C" "Bank of America Corporation/Banker" 
    [5] "Bank of America Corporation/Senior"  "Bank of America Corporation/Vice Ch"
    [7] "BANK OF AMERICA CORPORATION"        
    > 
    > agrep("BANK OF AMERICA CORP",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.05)
    [1] "BANK OF AMERICA CORP"                "Bank of America Corporation/Sales C"
    [3] "Bank of America Corporation/Banker"  "Bank of America Corporation/Senior" 
    [5] "Bank of America Corporation/Vice Ch" "BANK OF AMERICA CORPORATION"

Then below is the "1st Capital Bank" for "max.distance" of "0.2", "0.1", and "0.05": 然后下面是“第一资本银行”，“max.distance”为“0.2”，“0.1”和“0.05”： 

    > agrep("1st Capital Bank",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.2)
      [1] "HURST CAPITAL PARTNERS"             
      [2] "SOY CAPITAL BANK"                   
      [3] "FIRST CAPITOL BANK OF VICTOR"       
      [4] "OSTERWEIS CAPITAL MANAGEMENT"       
      [5] "1ST NATIONAL BANK"                  
      [6] "FIRST CAPITAL BANK"                 
      [7] "SEATTLE 1ST NAT'L BANK"             
      [8] "FIELD POINT CAPITAL MANAGEMENT"     
      [9] "SUMMERSET CAPITAL MANAGEMENT"       
     [10] "AMERIQUEST CAPITAL ASSOC"           
     [11] "BB&T CAPITAL MARKETS"               
     [12] "HUGHES CAPITAL MANAGEMENT"          
     [13] "WELLS CAPITAL MANAGEMENT"           
     [14] "SUPERIOR ST CAPITAL ADVISORS"       
     [15] "ORMES CAPITAL MARKETS INC"          
     [16] "1ST NAT'L BANK OF IL"               
     [17] "ADVENT CAPITAL MANAGEMENT"          
     [18] "1ST CAPITOL BANK"                   
     [19] "BIONDI REISS CAPITAL MANAGEMENT"    
     [20] "CCYBYS CAPITAL MARKETS"             
     [21] "SEACOAST CAPITAL PARTNERS"          
     [22] "DOUGLAS CAPITAL MANAGEMENT"         
     [23] "HIGHFIELDS CAPITAL MANAGEMENT"      
     [24] "PRECEPT CAPITAL MANAGEMENT LP"      
     [25] "AUGUST CAPITAL MANAGEMENT"          
     [26] "SAKSA CAPITAL MANAGEMENT"           
     [27] "IMS CAPITAL MANAGEMENT"             
     [28] "TRENT CAPITAL MANAGEMENT"           
     [29] "Ormes Capital Management"           
     [30] "GARNET CAPITAL MANAGEMENT LLC"      
     [31] "INTERFASE CAPITAL MANAGERS"         
     [32] "RJS CAPITAL MANAGEMENT INC"         
     [33] "1ST NATIONAL BANK OF DE KALB"       
     [34] "1ST NAT'L BANK OF PHILLIPS CO"      
     [35] "1ST NAT'L BANK OF OKLAHOMA"         
     [36] "PROGRESS CAPITAL MANAGEMENT INC"    
     [37] "CAPITAL BANK & TRUST"               
     [38] "1ST NATL BANK"                      
     [39] "ASB Capital Management/Real Estate" 
     [40] "Sears Capital Management"           
     [41] "Osterweis Capital Management/Invest"
     [42] "Cerberus Capital Management LP/Asse"
     [43] "LVS Capital Management/President"   
     [44] "1st Central Bank/Banker"            
     [45] "Summit Capital Management"          
     [46] "Orwes Capital Markets/Stockbroker"  
     [47] "Ormes Capital Management/Investment"
     [48] "Nevis Capital Management/Investment"
     [49] "Duncan Hurst Capital Management"    
     [50] "Progress Capital Management/Preside"
     [51] "Cerberus Capital Management LP"     
     [52] "Wit Capital/Banker"                 
     [53] "Ormes Capital Markets Inc."         
     [54] "Ormes Capital Markets/President & C"
     [55] "Berents & Hess Capital Management"  
     [56] "Progress Capital Management/Venture"
     [57] "First Capital Bank of KY"           
     [58] "Foothill Capital/Banker"            
     [59] "Pequot Capital Management/Equity Re"
     [60] "First Dominion Capital/Banking"     
     [61] "Greenwhich Capital/Banker"          
     [62] "Veritas Capital Management/Banker"  
     [63] "Veritas Capital Management/Investme"
     [64] "Lesese Capital Management/Investmen"
     [65] "Douglas Capital Management/Investme"
     [66] "FIRST NATINAL BANK OF AMARILLO"     
     [67] "NEVIS CAPITAL MANAGEMENT"           
     [68] "VERITAS CAPITAL MANAGEMENT"         
     [69] "SIEBERT CAPITAL MARKETS"            
     [70] "HOURGLASS CAPITAL MANAGEMENT"       
     [71] "1ST NATIONAL BANK DALHART"          
     [72] "TEXAS CAPITAL BANK"                 
     [73] "NICHOLAS CAPITAL MANAGEMENT"        
     [74] "CERBUS CAPITAL MANAGEMENT"          
     [75] "CROESUS CAPITAL MANAGEMENT"         
     [76] "EAST WEST CAPITAL ASSOCIATES INC"   
     [77] "PRENDERGAST CAPITAL MANAGEMENT"     
     [78] "NANTUCKET CAPITAL MANAGEMENT"       
     [79] "1ST NATIONAL BANK TEMPLE"           
     [80] "ENTRUST CAPITAL INC"                
     [81] "1ST NATIONAL BANK OF IL"            
     [82] "SIMMS CAPITAL MANAGEMENT"           
     [83] "FIRST CAPITAL ADVISORS"             
     [84] "FIRST CAPITAL MANAGEMENT LTD"       
     [85] "1ST NATIONAL BANK & TRUST"          
     [86] "PENTECOST CAPITAL MANAGEMENT INC"   
     [87] "EAST-WEST CAPITAL ASSOCIATES"       
     [88] "1ST NAT'L BANK OF JOLIET"           
     [89] "FIRST CAPITOL BANK OF VICTO"        
     [90] "FIRST CAPITAL FINANCIAL"            
     [91] "PACIFIC COAST CAPITAL PARTNERS"     
     [92] "FIRST CAPITOL BANK"                 
     [93] "FIRST CAPITAL ENGINEERING"          
     [94] "MIDWEST CAPITOL MANAGEMENT"         
     [95] "PEQUOT CAPITAL MANAGEMENT"          
     [96] "AGGOTT CAPITAL MANAGEMENT"          
     [97] "SIMMS CAPITAL MANAGEMENT INC"       
     [98] "PHILLIPS CAPITAL MANAGEMENT LLC"    
     [99] "1ST NATIONAL BANK OF COLD SP"       
    [100] "SOY CAPITOL BANK"                   
    > 
    > agrep("1st Capital Bank",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.1)
    [1] "FIRST CAPITOL BANK OF VICTOR" "FIRST CAPITAL BANK"          
    [3] "1ST CAPITOL BANK"             "First Capital Bank of KY"    
    [5] "TEXAS CAPITAL BANK"           "FIRST CAPITOL BANK OF VICTO" 
    [7] "FIRST CAPITOL BANK"          
    > 
    > agrep("1st Capital Bank",C1999_0[,2],ignore.case = TRUE, value = TRUE,fixed = TRUE,max.distance =0.05)
    [1] "FIRST CAPITAL BANK"       "1ST CAPITOL BANK"        
    [3] "First Capital Bank of KY"

As you can see, it's really difficult to find the common precision value for "max.distance" to apply for every each string such as "BANK OF AMERICA CORP" and "1st Capital Bank". 正如您所看到的，很难找到“max.distance”的公共精度值来申请每个字符串，例如“BANK OF AMERICA CORP”和“1st Capital Bank”。 Also I have far much more firm names other than these two which is the reason why I am having difficulty in finding the common precision value and command for fuzzy string match. 除了这两个之外，我还有更多的公司名称，这就是为什么我很难找到模糊字符串匹配的公共精度值和命令。

The original data file for C1999_0 is too huge to attach so I think only using the output values as shown above would be sufficient to replicate. C1999_0的原始数据文件太大而无法附加，所以我认为只使用上面显示的输出值就足以复制了。

I know there are several sub categories to manipulate such as cost, substitutions, insertion, etc. but they did not make much difference with only changing the "max.distance" value itself. 我知道有几个子类别可以操作，例如成本，替换，插入等，但它们与仅更改“max.distance”值本身没有太大区别。

I would really appreciate if I can get help on this! 如果我能得到帮助，我将非常感激！

2 个解决方案

解决方案1
 3  2018-09-22 19:16:54

An issue with agrep is that it is like grep as documented in help("grep") 有一个问题agrep是，它像grep如中记录help("grep")

Since someone who read the description carelessly even filed a bug report on it, do note that this matches substrings of each element of x (just as grep does) and not whole elements. 由于有人不经意地阅读了描述，甚至提交了错误报告，请注意这与x的每个元素的子串（就像grep一样）而不是整个元素。 See also adist in package utils , which optionally returns the offsets of the matched substrings. 另请参阅包utils中的 adist ，它可选择返回匹配的子字符串的偏移量。

This seem to be the issue in your latter example since of you have many names that contains "Capital" or "Bank" or both. 这似乎是后一个例子中的问题，因为你有许多包含“Capital”或“Bank”或两者的名字。 What I would do, is to use to compute the Levenshtein distance (which is what agrep does or a generalized version and only for substrings ) and take the ones with shortest distance. 我要做的是，用来计算Levenshtein距离 （这是agrep所做的或广义版本， 仅适用于子串 ）并采用距离最短的距离。 Eg, 例如， 

C1999 <- c("HURST CAPITAL PARTNERS", "SOY CAPITAL BANK", "FIRST CAPITOL BANK OF VICTOR", "OSTERWEIS CAPITAL MANAGEMENT", "1ST NATIONAL BANK", "FIRST CAPITAL BANK", "SEATTLE 1ST NAT'L BANK", "FIELD POINT CAPITAL MANAGEMENT", "SUMMERSET CAPITAL MANAGEMENT", "AMERIQUEST CAPITAL ASSOC", "BB&T CAPITAL MARKETS", "HUGHES CAPITAL MANAGEMENT", "WELLS CAPITAL MANAGEMENT", "SUPERIOR ST CAPITAL ADVISORS", "ORMES CAPITAL MARKETS INC", "1ST NAT'L BANK OF IL", "ADVENT CAPITAL MANAGEMENT", "1ST CAPITOL BANK", "BIONDI REISS CAPITAL MANAGEMENT", "CCYBYS CAPITAL MARKETS", "SEACOAST CAPITAL PARTNERS", "DOUGLAS CAPITAL MANAGEMENT", "HIGHFIELDS CAPITAL MANAGEMENT", "PRECEPT CAPITAL MANAGEMENT LP", "AUGUST CAPITAL MANAGEMENT", "SAKSA CAPITAL MANAGEMENT", "IMS CAPITAL MANAGEMENT", "TRENT CAPITAL MANAGEMENT", "Ormes Capital Management", "GARNET CAPITAL MANAGEMENT LLC", "INTERFASE CAPITAL MANAGERS", "RJS CAPITAL MANAGEMENT INC", "1ST NATIONAL BANK OF DE KALB", "1ST NAT'L BANK OF PHILLIPS CO", "1ST NAT'L BANK OF OKLAHOMA", "PROGRESS CAPITAL MANAGEMENT INC", "CAPITAL BANK & TRUST", "1ST NATL BANK", "ASB Capital Management/Real Estate", "Sears Capital Management", "Osterweis Capital Management/Invest", "Cerberus Capital Management LP/Asse", "LVS Capital Management/President", "1st Central Bank/Banker", "Summit Capital Management", "Orwes Capital Markets/Stockbroker", "Ormes Capital Management/Investment", "Nevis Capital Management/Investment", "Duncan Hurst Capital Management", "Progress Capital Management/Preside", "Cerberus Capital Management LP", "Wit Capital/Banker", "Ormes Capital Markets Inc.", "Ormes Capital Markets/President & C", "Berents & Hess Capital Management", "Progress Capital Management/Venture", "First Capital Bank of KY", "Foothill Capital/Banker", "Pequot Capital Management/Equity Re", "First Dominion Capital/Banking", "Greenwhich Capital/Banker", "Veritas Capital Management/Banker", "Veritas Capital Management/Investme", "Lesese Capital Management/Investmen", "Douglas Capital Management/Investme", "FIRST NATINAL BANK OF AMARILLO", "NEVIS CAPITAL MANAGEMENT", "VERITAS CAPITAL MANAGEMENT", "SIEBERT CAPITAL MARKETS", "HOURGLASS CAPITAL MANAGEMENT", "1ST NATIONAL BANK DALHART", "TEXAS CAPITAL BANK", "NICHOLAS CAPITAL MANAGEMENT", "CERBUS CAPITAL MANAGEMENT", "CROESUS CAPITAL MANAGEMENT", "EAST WEST CAPITAL ASSOCIATES INC", "PRENDERGAST CAPITAL MANAGEMENT", "NANTUCKET CAPITAL MANAGEMENT", "1ST NATIONAL BANK TEMPLE", "ENTRUST CAPITAL INC", "1ST NATIONAL BANK OF IL", "SIMMS CAPITAL MANAGEMENT", "FIRST CAPITAL ADVISORS", "FIRST CAPITAL MANAGEMENT LTD", "1ST NATIONAL BANK & TRUST", "PENTECOST CAPITAL MANAGEMENT INC", "EAST-WEST CAPITAL ASSOCIATES", "1ST NAT'L BANK OF JOLIET", "FIRST CAPITOL BANK OF VICTO", "FIRST CAPITAL FINANCIAL", "PACIFIC COAST CAPITAL PARTNERS", "FIRST CAPITOL BANK", "FIRST CAPITAL ENGINEERING", "MIDWEST CAPITOL MANAGEMENT", "PEQUOT CAPITAL MANAGEMENT", "AGGOTT CAPITAL MANAGEMENT", "SIMMS CAPITAL MANAGEMENT INC", "PHILLIPS CAPITAL MANAGEMENT LLC", "1ST NATIONAL BANK OF COLD SP", "SOY CAPITOL BANK")

func <- function(x, y, tol = 0L){
  require(stringdist)
  dista <- stringdist::stringdist(x, y, method = "lv")
  min_dista <- min(dista)
  y[dista <= min_dista + tol]
}
func("1st Capital Bank", C1999)
#R [1] "Wit Capital/Banker"
func("1st Capital Bank", C1999, 4L)
#R [1] "Wit Capital/Banker"       "First Capital Bank of KY"
func("1st Capital Bank", C1999, 10L)
#R  [1] "SOY CAPITAL BANK"           "1ST NATIONAL BANK"         
#R  [3] "FIRST CAPITAL BANK"         "1ST CAPITOL BANK"          
#R  [5] "Ormes Capital Management"   "1ST NATL BANK"             
#R  [7] "Sears Capital Management"   "1st Central Bank/Banker"   
#R  [9] "Summit Capital Management"  "Wit Capital/Banker"        
#R [11] "Ormes Capital Markets Inc." "First Capital Bank of KY"  
#R [13] "Foothill Capital/Banker"    "Greenwhich Capital/Banker" 
#R [15] "TEXAS CAPITAL BANK"         "FIRST CAPITOL BANK"        
#R [17] "SOY CAPITOL BANK" 

# ignoring cases
func <- function(x, y, tol = 0L){
  require(stringdist)
  dista <- stringdist::stringdist(tolower(x), tolower(y), method = "lv")
  min_dista <- min(dista)
  y[dista <= min_dista + tol]
}
func("1st Capital Bank", C1999, 0L)
#R [1] "1ST CAPITOL BANK"

The tol parameter in the func controls the if you want to include examples that are tol further away from the minimum Levenshtein distance. 该tol在参数func如果要包括作为例子控制tol从最小Levenshtein距离渐行渐远。 I see that I did not answer exactly what you asked for ( How can I get the precise common “max.distance” value for fuzzy string matching using agrep ? ) but I think my answer might be what you are looking for. 我看到我没有准确回答你的要求（ 如何使用agrep获得模糊字符串匹配的精确常用“max.distance”值？ ）但我认为我的答案可能就是你要找的。

I use stringdist::stringdist instead of adist since the former seems to be faster. 我使用stringdist::stringdist而不是adist因为前者似乎更快。 It can still be a bit slow and I wish there where an R package where you could set a maximum distance but I have not come across such a package. 它仍然可能有点慢，我希望在那里你可以设置最大距离的R包，但我没有遇到过这样的包。 This could make the computation of the (then capped) Levenshtein distance much faster. 这可以使得（然后加盖的）Levenshtein距离的计算更快。

解决方案2
 2 已采纳  2018-09-26 02:44:50

It seems possible that this is an unsolvable problem as stated, that there isn't one max.distance that will work well for all input strings. 这似乎是一个无法解决的问题，如上所述，没有一个max.distance可以很好地适用于所有输入字符串。

It might be worth trying a method like tf-idf to identify the unusualness of your strings and scale your max.distance to that. 可能值得尝试像tf-idf这样的方法来识别字符串的异常，并将你的max.distance缩放到那个。 So "Ziggurat Mutual" might be given more leeway for variation than "First Bank National" which is more generic. 因此，“Ziggurat Mutual”可能比“First Bank National”更具通用性。

You might also consider using the fuzzyjoin package, which offers some quick ways to try different options. 您也可以考虑使用fuzzyjoin包，它提供了一些尝试不同选项的快速方法。 For instance you might try: 例如，您可以尝试： 

df <- c("HURST CAPITAL PARTNERS", "SOY CAPITAL BANK", "FIRST CAPITOL BANK OF VICTOR", "OSTERWEIS CAPITAL MANAGEMENT", "1ST NATIONAL BANK", "FIRST CAPITAL BANK", "SEATTLE 1ST NAT'L BANK", "FIELD POINT CAPITAL MANAGEMENT", "SUMMERSET CAPITAL MANAGEMENT", "AMERIQUEST CAPITAL ASSOC", "BB&T CAPITAL MARKETS", "HUGHES CAPITAL MANAGEMENT", "WELLS CAPITAL MANAGEMENT", "SUPERIOR ST CAPITAL ADVISORS", "ORMES CAPITAL MARKETS INC", "1ST NAT'L BANK OF IL", "ADVENT CAPITAL MANAGEMENT", "1ST CAPITOL BANK", "BIONDI REISS CAPITAL MANAGEMENT", "CCYBYS CAPITAL MARKETS", "SEACOAST CAPITAL PARTNERS", "DOUGLAS CAPITAL MANAGEMENT", "HIGHFIELDS CAPITAL MANAGEMENT", "PRECEPT CAPITAL MANAGEMENT LP", "AUGUST CAPITAL MANAGEMENT", "SAKSA CAPITAL MANAGEMENT", "IMS CAPITAL MANAGEMENT", "TRENT CAPITAL MANAGEMENT", "Ormes Capital Management", "GARNET CAPITAL MANAGEMENT LLC", "INTERFASE CAPITAL MANAGERS", "RJS CAPITAL MANAGEMENT INC", "1ST NATIONAL BANK OF DE KALB", "1ST NAT'L BANK OF PHILLIPS CO", "1ST NAT'L BANK OF OKLAHOMA", "PROGRESS CAPITAL MANAGEMENT INC", "CAPITAL BANK & TRUST", "1ST NATL BANK", "ASB Capital Management/Real Estate", "Sears Capital Management", "Osterweis Capital Management/Invest", "Cerberus Capital Management LP/Asse", "LVS Capital Management/President", "1st Central Bank/Banker", "Summit Capital Management", "Orwes Capital Markets/Stockbroker", "Ormes Capital Management/Investment", "Nevis Capital Management/Investment", "Duncan Hurst Capital Management", "Progress Capital Management/Preside", "Cerberus Capital Management LP", "Wit Capital/Banker", "Ormes Capital Markets Inc.", "Ormes Capital Markets/President & C", "Berents & Hess Capital Management", "Progress Capital Management/Venture", "First Capital Bank of KY", "Foothill Capital/Banker", "Pequot Capital Management/Equity Re", "First Dominion Capital/Banking", "Greenwhich Capital/Banker", "Veritas Capital Management/Banker", "Veritas Capital Management/Investme", "Lesese Capital Management/Investmen", "Douglas Capital Management/Investme", "FIRST NATINAL BANK OF AMARILLO", "NEVIS CAPITAL MANAGEMENT", "VERITAS CAPITAL MANAGEMENT", "SIEBERT CAPITAL MARKETS", "HOURGLASS CAPITAL MANAGEMENT", "1ST NATIONAL BANK DALHART", "TEXAS CAPITAL BANK", "NICHOLAS CAPITAL MANAGEMENT", "CERBUS CAPITAL MANAGEMENT", "CROESUS CAPITAL MANAGEMENT", "EAST WEST CAPITAL ASSOCIATES INC", "PRENDERGAST CAPITAL MANAGEMENT", "NANTUCKET CAPITAL MANAGEMENT", "1ST NATIONAL BANK TEMPLE", "ENTRUST CAPITAL INC", "1ST NATIONAL BANK OF IL", "SIMMS CAPITAL MANAGEMENT", "FIRST CAPITAL ADVISORS", "FIRST CAPITAL MANAGEMENT LTD", "1ST NATIONAL BANK & TRUST", "PENTECOST CAPITAL MANAGEMENT INC", "EAST-WEST CAPITAL ASSOCIATES", "1ST NAT'L BANK OF JOLIET", "FIRST CAPITOL BANK OF VICTO", "FIRST CAPITAL FINANCIAL", "PACIFIC COAST CAPITAL PARTNERS", "FIRST CAPITOL BANK", "FIRST CAPITAL ENGINEERING", "MIDWEST CAPITOL MANAGEMENT", "PEQUOT CAPITAL MANAGEMENT", "AGGOTT CAPITAL MANAGEMENT", "SIMMS CAPITAL MANAGEMENT INC", "PHILLIPS CAPITAL MANAGEMENT LLC", "1ST NATIONAL BANK OF COLD SP", "SOY CAPITOL BANK")

library(dplyr); library(fuzzyjoin)
df <- df %>% as_data_frame()

df %>%
  # Allowable methods include osa, lv, dl, hamming, lcs, qgram, 
  #    cosine, jaccard, jw, soundex
  fuzzyjoin::stringdist_inner_join(df, method = "lv", distance_col = "distance", max_dist = 4) %>%
  filter(distance > 0)

Joining by: "value"
# A tibble: 70 x 3
   value.x                      value.y                     distance
   <chr>                        <chr>                          <dbl>
 1 SOY CAPITAL BANK             1ST CAPITOL BANK                   4
 2 SOY CAPITAL BANK             SOY CAPITOL BANK                   1
 3 FIRST CAPITOL BANK OF VICTOR FIRST CAPITOL BANK OF VICTO        1
 4 1ST NATIONAL BANK            1ST NATL BANK                      4
 5 FIRST CAPITAL BANK           1ST CAPITOL BANK                   4
 6 FIRST CAPITAL BANK           FIRST CAPITOL BANK                 1
 7 HUGHES CAPITAL MANAGEMENT    DOUGLAS CAPITAL MANAGEMENT         4
 8 HUGHES CAPITAL MANAGEMENT    AUGUST CAPITAL MANAGEMENT          4
 9 WELLS CAPITAL MANAGEMENT     IMS CAPITAL MANAGEMENT             4
10 WELLS CAPITAL MANAGEMENT     NEVIS CAPITAL MANAGEMENT           3

...to experiment with potential non-exact matches among your example list. ...在您的示例列表中尝试潜在的非完全匹配。

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