Databases
StackOverflow2013
Postsdbo
PO
 

Note that there are some explanatory texts on larger screens.

plurals
  1. PO
    primarykey
    data
    text
    <p>You need to normalize the groups. In each group, pick one word or coding that represents the group. Then group the words by their representative.</p> <p>Some possible ways:</p> <ul> <li>Pick the first encountered word.</li> <li>Pick the lexicographic first word.</li> <li>Derive a pattern for all the words.</li> <li>Pick an unique index.</li> <li>Use the <a href="http://en.wikipedia.org/wiki/Soundex" rel="nofollow" title="WP: Soundex">soundex</a> as pattern.</li> </ul> <p>Grouping the words could be difficult, though. If A is similar to B, and B is similar to C, A and C is not necessarily similar to each other. If B is the representative, both A and C could be included in the group. But if A or C is the representative, the other could not be included.</p> <hr> <p>Going by the first alternative (first encountered word):</p> <pre><code>class Seeder: def __init__(self): self.seeds = set() self.cache = dict() def get_seed(self, word): LIMIT = 2 seed = self.cache.get(word,None) if seed is not None: return seed for seed in self.seeds: if self.distance(seed, word) &lt;= LIMIT: self.cache[word] = seed return seed self.seeds.add(word) self.cache[word] = word return word def distance(self, s1, s2): l1 = len(s1) l2 = len(s2) matrix = [range(zz,zz + l1 + 1) for zz in xrange(l2 + 1)] for zz in xrange(0,l2): for sz in xrange(0,l1): if s1[sz] == s2[zz]: matrix[zz+1][sz+1] = min(matrix[zz+1][sz] + 1, matrix[zz][sz+1] + 1, matrix[zz][sz]) else: matrix[zz+1][sz+1] = min(matrix[zz+1][sz] + 1, matrix[zz][sz+1] + 1, matrix[zz][sz] + 1) return matrix[l2][l1] import itertools def group_similar(words): seeder = Seeder() words = sorted(words, key=seeder.get_seed) groups = itertools.groupby(words, key=seeder.get_seed) return [list(v) for k,v in groups] </code></pre> <p><strong>Example:</strong></p> <pre><code>import pprint print pprint.pprint(group_similar([ 'the', 'be', 'to', 'of', 'and', 'a', 'in', 'that', 'have', 'I', 'it', 'for', 'not', 'on', 'with', 'he', 'as', 'you', 'do', 'at', 'this', 'but', 'his', 'by', 'from', 'they', 'we', 'say', 'her', 'she', 'or', 'an', 'will', 'my', 'one', 'all', 'would', 'there', 'their', 'what', 'so', 'up', 'out', 'if', 'about', 'who', 'get', 'which', 'go', 'me', 'when', 'make', 'can', 'like', 'time', 'no', 'just', 'him', 'know', 'take', 'people', 'into', 'year', 'your', 'good', 'some', 'could', 'them', 'see', 'other', 'than', 'then', 'now', 'look', 'only', 'come', 'its', 'over', 'think', 'also', 'back', 'after', 'use', 'two', 'how', 'our', 'work', 'first', 'well', 'way', 'even', 'new', 'want', 'because', 'any', 'these', 'give', 'day', 'most', 'us' ]), width=120) </code></pre> <p><strong>Output:</strong></p> <pre><code>[['after'], ['also'], ['and', 'a', 'in', 'on', 'as', 'at', 'an', 'one', 'all', 'can', 'no', 'want', 'any'], ['back'], ['because'], ['but', 'about', 'get', 'just'], ['first'], ['from'], ['good', 'look'], ['have', 'make', 'give'], ['his', 'her', 'if', 'him', 'its', 'how', 'us'], ['into'], ['know', 'new'], ['like', 'time', 'take'], ['most'], ['of', 'I', 'it', 'for', 'not', 'he', 'you', 'do', 'by', 'we', 'or', 'my', 'so', 'up', 'out', 'go', 'me', 'now'], ['only'], ['over', 'our', 'even'], ['people'], ['say', 'she', 'way', 'day'], ['some', 'see', 'come'], ['the', 'be', 'to', 'that', 'this', 'they', 'there', 'their', 'them', 'other', 'then', 'use', 'two', 'these'], ['think'], ['well'], ['what', 'who', 'when', 'than'], ['with', 'will', 'which'], ['work'], ['would', 'could'], ['year', 'your']] </code></pre>
    singulars
    1. This table or related slice is empty.
    1. POFuzzy Group By, Grouping Similar Words
      singulars
      1. PTQuestion
    1. PTAnswer
    1. USMarkus Jarderot
    1. USMarkus Jarderot
    plurals
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. POFuzzy Group By, Grouping Similar Words
      singulars
      1. PTQuestion
    1. This table or related slice is empty.
    1. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    2. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    3. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    1. COFinding the groups would be the hard part. I guess I could use a clustering algorithm that takes e.g. levenshtein distance as distance measure. After clusters are identified I pick one of the words (any of them) as representative for that group.
      singulars
      1. PO
      1. USuser423805
    2. COAlternatively, you can use the mean pairwise Levenshtein distance between the words in two groups as the distance measure between them (many hierarchical clustering algorithms work this way). The max pairwise distance might also work.
      singulars
      1. PO
      1. USDavid Robinson
    3. COLovely explanation of representative of group. However, I would recommend (double) metaphone over soundex. +1
      singulars
      1. PO
      1. USJon Clements
 

Querying!

 
Guidance
A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload