Access可以使用模糊逻辑 [英] Can Access use Fuzzy Logic
问题描述
这是问题所在：
我有大约20个MS excel电子表格，每行包含一条记录。
这些记录是手工输入的呼叫中心的人。
问题是，有可能并且有重复的电话号码和电子邮件
并且甚至可以解决人名。我需要筛选所有这些数据
（大约300,000条记录并使用模糊逻辑将其分解，以便
i只有唯一记录。
我可以使用Access或者对所有这些数据进行排序吗？
ca ****** @ gmail.com 写道：这是问题：
我有大约20个MS excel电子表格，每行包含一条记录。
这些记录是由呼叫中心的人员手动输入的。
问题是，可以存在重复的电话号码和电子邮件
和地址甚至人名。我需要筛选所有这些数据
（大约300,000多条记录并使用模糊逻辑来分解它，所以我有只有独特的记录。
我可以使用Access或者对所有这些数据进行排序吗？
模糊逻辑基本上和确定一样概率窝点ity
凭经验分配。当然，被认为是冷的温度因人而异，也因人而异。我认为你需要的只是一个Public函数，它根据函数中的模糊逻辑为每个
记录赋值。然后你可以使用
函数的返回值来识别重复的记录，其中模糊意义上使用了重复的
。
James A. Fortune
CD********@FortuneJames.com
不清楚你想做什么，但我认为你的
问题的答案是肯定的。
您可以将数据导入Access，然后使用SQL将记录减去
到一个不同的集合。通过查看Microsoft下的帮助文件内容查看SELECT DISTINCT上的帮助文件和
SELECT DISTINCTROW
Jet SQL参考|概述| SQL保留字。
对于那么多记录，我会非常谨慎地导入激进元素
并且对每个记录的Field属性都要非常严格。我会建议你从格式化
的角度确保数据集的连续性，并在将它们移到
之前修剪Excel中的所有字段访问。你还需要注意Nulls，Empty Sets等等，但是如果你确定要从你的数据集中删除所有这些激进分子，那么
，
you可以在SQL中做任何你想要的东西，以获得一个独特的
记录集，包括使用前面提到的模糊逻辑来识别带有正确措辞的WHERE子句的
记录。
ca******@gmail.com 写道：这里是问题：
我有大约20个MS excel电子表格，每行包含一条记录。
这些记录是在呼叫中心的人进入的手。
问题是，有可能并且有重复的电话号码，电子邮件
和地址甚至人名。我需要筛选所有这些数据
（大约300,000条记录并使用模糊逻辑将其分解，以便我只有独特的记录。
我可以使用Access吗？或者对所有这些数据进行排序？
严格来说，可以在这里使用模糊逻辑，但我认为
可能会有一些聪明的启发式算法更简单。你没有提供详细信息
关于复制的性质，但是名称，
地址等的许多细微变化可以通过规范化值来清除通过删除额外的
空间，强制所有大写相同，强制所有手机
数字为相同格式等。
-Will Dwinnell
http://will.dwinnell.com
Here''s the issue:
I have roughly 20 MS excel spreadsheets, each row contains a record.
These records were hand entered by people in call centers.
The problem is, there can and are duplicate phone numbers, and emails
and addresses even person names. I need to sift through all this data
(roughly 300,000+ records and use fuzzy logic to break it down, so that
i have only unique records.
Can I use Access or what to sort through all this data?
ca******@gmail.com wrote:Here''s the issue:
I have roughly 20 MS excel spreadsheets, each row contains a record.
These records were hand entered by people in call centers.
The problem is, there can and are duplicate phone numbers, and emails
and addresses even person names. I need to sift through all this data
(roughly 300,000+ records and use fuzzy logic to break it down, so that
i have only unique records.
Can I use Access or what to sort through all this data?
Fuzzy logic is basically the same as determining a probability density
distribution empirically. Of course, a temperature that is considered
''cold'' varies from month to month as well as from person to person. I
think all you need is a Public function that assigns a value to each
record based on the fuzzy logic in the function. Then you can use the
function''s return value to identify duplicate records where duplicate
is used in a fuzzy sense.
James A. Fortune
CD********@FortuneJames.com
Not really clear on what you want to do, but I think the answer to your
question is Yes.
You can import the data to Access and then use SQL to pare the records
down to a distinct set. Check out the help file on SELECT DISTINCT and
SELECT DISTINCTROW by looking in the help file contents under Microsoft
Jet SQL Reference|Overview|SQL Reserved Words.
WIth that many records, I''d be very cautious about importing radicals
and be very strict in your Field attributes for each Record. I''d
suggest you ensure continuity of your dataset from a formatting
perspective and Trim all the fields in Excel before moving them to
Access. You''ll also have to watch out for Nulls, Empty Sets, etc., but
if you are sure to remove all radicals like these from your dataset,
you can do pretty well anything you want in SQL to arrive at a unique
recordset including using the aforementioned fuzzy logic to identify
records with properly phrased WHERE clauses.
ca******@gmail.com wrote:Here''s the issue:
I have roughly 20 MS excel spreadsheets, each row contains a record.
These records were hand entered by people in call centers.
The problem is, there can and are duplicate phone numbers, and emails
and addresses even person names. I need to sift through all this data
(roughly 300,000+ records and use fuzzy logic to break it down, so that
i have only unique records.
Can I use Access or what to sort through all this data?
Strictly speaking, one could use fuzzy logic here, but I would think
that a few clever heuristics might be simpler. You don''t give details
on the nature of the duplication, but many slight variations on name,
address, etc. can be cleared up by normalizing values by removing extra
space, forcing all capitalization to be the same, forcing all phone
numbers to the same format, etc.
-Will Dwinnell
http://will.dwinnell.com
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