Data mining is simply filtering thr

Data mining is simply filtering through large
amounts of raw data for useful information that
gives businesses a competitive edge. This
information is made up of meaningful patterns
and trends that are already in the data but were
previously unseen.
The most popular tool used when mining is
artificial intelligence (AI). AI technologies try to
work the way the human brain works, by making
intelligent guesses, learning by example, and
using deductive reasoning. Some of the more
popular AI methods used in data mining include
neural networks, clustering, and decision trees.
Neural networks look at the rules of using data,
which are based on the connections found or on
a sample set of data. As a result, the software
continually analyses value and compares it to the
other factors, and it compares these factors
repeatedly until it finds patterns emerging. These
patterns are known as rules. The software then
looks for other patterns based on these rules or
sends out an alarm when a trigger value is hit.
Clustering divides data into groups based on
similar features or limited data ranges. Clusters
are used when data isn't labelled in a way that is
favourable to mining. For instance, an insurance
company that wants to find instances of fraud
wouldn't have its records labelled as fraudulent
or not fraudulent. But after analysing patterns
within clusters, the mining software can start to
figure out the rules that point to which claims
are likely to be false.
Decision trees, like clusters, separate the data
into subsets and then analyse the subsets to
divide them into further subsets, and so on (for
a few more levels). The final subsets are then
small enough that the mining process can find
interesting patterns and relationships within the
data.
Once the data to be mined is identified, it
should be cleansed. Cleansing data frees it from
duplicate information and erroneous data. Next,
the data should be stored in a uniform format
within relevant categories or fields. Mining tools
can work with all types of data storage, from
large data warehouses to smaller desktop
databases to flat files. Data warehouses and data
marts are storage methods that involve archiving
large amounts of data in a way that makes it easy
to access when necessary.
When the process is complete, the mining
software generates a report. An analyst goes over
the report to see if further work needs to be
done, such as refining parameters, using other
data analysis tools to examine the data, or even
scrapping the data if it's unusable. If no further
work is required, the report proceeds to the
decision makers for appropriate action.
The power of data mining is being used for
many purposes, such as analysing Supreme
Court decisions, discovering patterns in health
care, pulling stories about competitors from
newswires, resolving bottlenecks in production
processes, and analysing sequences in the human
genetic makeup. There really is no limit to the
type of business or area of study where data
mining can be beneficial.

Результаты (русский) 1:

Интеллектуальный анализ данных просто фильтрации через большиеколичество необработанных данных за полезную информацию,дает конкурентное преимущество бизнеса. ЭтоИнформация состоит из значимых шаблонови тенденции, которые уже находятся в данных, но былиранее невидимых.Наиболее популярный инструмент, используемый при добычеискусственный интеллект (ии). AI технологий пытаютсяработе так, как человеческий мозг работает, делаяинтеллигентая(ый) догадки, обучения, например, ис помощью дедуктивного мышления. Некоторые из наиболеепопулярные методы AI, используемых в интеллектуальном анализе данных включаютНейронные сети, кластеризация и деревья принятия решений.Нейронные сети смотреть на правила использования данных,которые основываются на соединения нашли или наобразец набора данных. Как результат, программное обеспечениепостоянно анализирует значение и сравнивает его сдругие факторы и сравнивает эти факторыдо тех пор, пока он находит новые шаблоны. ЭтиУзоры известны как правила. Затем программное обеспечениевыглядит для других моделей, основанных на этих правил илипосылает сигнал тревоги, когда хит значение триггера.Кластеризация разделяет данные на группы, основанные нааналогичные функции или ограниченных данных диапазонов. Кластерыиспользуются при данных не помечены таким образом, что являетсяблагоприятные для горнодобывающей промышленности. Например страхованиеКомпания, которая хочет найти случаи мошенничестване имеют свои записи помечены как мошенничестваили не мошенничества. Но после анализа шаблоновв кластерах интеллектуального программного обеспечения может начатьвыяснить, правила, которые указывают на какие претензииare likely to be false.Decision trees, like clusters, separate the datainto subsets and then analyse the subsets todivide them into further subsets, and so on (fora few more levels). The final subsets are thensmall enough that the mining process can findinteresting patterns and relationships within thedata.Once the data to be mined is identified, itshould be cleansed. Cleansing data frees it fromduplicate information and erroneous data. Next,the data should be stored in a uniform formatwithin relevant categories or fields. Mining toolscan work with all types of data storage, fromlarge data warehouses to smaller desktopdatabases to flat files. Data warehouses and datamarts are storage methods that involve archivinglarge amounts of data in a way that makes it easyto access when necessary.When the process is complete, the miningsoftware generates a report. An analyst goes overthe report to see if further work needs to bedone, such as refining parameters, using otherdata analysis tools to examine the data, or evenscrapping the data if it's unusable. If no furtherwork is required, the report proceeds to thedecision makers for appropriate action.The power of data mining is being used formany purposes, such as analysing SupremeCourt decisions, discovering patterns in healthcare, pulling stories about competitors fromnewswires, resolving bottlenecks in productionprocesses, and analysing sequences in the humangenetic makeup. There really is no limit to thetype of business or area of study where datamining can be beneficial.