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Five Data Mining Techniques That Benefit Your Business


Data mining is a buzzword that often is used to describe the entire range of big data analytics, including collection, extraction, analysis and statistics.

There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. The type of analysis you run depends on the type of business issue you are reseaching. Different analyses will deliver different outcomes and as result provide different insights. One of the common ways to recover valuable insights is via the process of data mining. Data mining specifically refers to the discovery of previously unknown or developing patterns, unusual incidents or interdependencies. Therefore it is important to have a clear understanding of what data mining is before you develop your big data strategy. 

The most important objective of any data mining process is to find useful information that is easily understood in large data sets. There are a five Techniques that apply to data mining:

I Anomaly or Outlier Detection

Anomaly detection refers to the search for data items in a dataset that do not match a projectedwww.insidedevices.org pattern or expected behaviour. Anomalies are also called outliers, exceptions, surprises or contaminants and they often provide critical and actionable information. An outlier is an object that deviates significantly from the general average within a dataset or a combination of data. It  numerically stands out  from the rest of the data and therefore, the outlier indicates that something is out of the ordinary and requires additional analysis.

Anomaly detection is used to detect fraud or risks within critical systems and they have all the characteristics to be of interest to an analyst. It can help find occurrences that could indicate:

  1. Fraudulent Actions, example detecting various types of credit card fraud
  2. Flawed Procedures, example: Health Insurrance Companies read more
  3. Erronuous Theory in Certain Areas.
  4. Bad Data: In large Datasets, a Small Amount of Outliers is common.

II. Association Rule Learning

Association rule learning enables the discovery of interesting relations (interdependencies) between different variables in large databases. Association rule learning uncovers hidden patterns in the data that can be used to identify variables within the data and the co-occurrences of different variables that appear with the greatest frequencies.

www.insidedevices.orgAssociation rule learning is often used in the retail industry when finding patterns in point-of-sales data. These patterns can be used when recommending new products to others based on what others have bought before or based on which products are bought together. If this is done correctly, it can help your organisation increase its conversion rate.

Example Of Association Rule Learning  In 2004(!!) Wallmart, discovered that Strawberry Pop-tarts sales increase by seven times prior to a hurricane. Since this discovery, Walmart places the Strawberry Pop-Tarts at the checkouts prior to a hurricane. When you see Pop-tarts you know a hurricane is coming your way!

III. Clustering Analysis

Clustering analysis is the process of identifying data sets that are similar to each other to understand the differences as well as the similarities within the data. Clusters have certain traits in common that can be used to improve targeting algorithms. For example, clusters of customers with similar buying behaviour can be targeted with similar products and services in order to increase the conversation rate.

Best  Example Of Clustering Analysis: Social Media (read more in this article) whether it is facebook, linkedin, spotify, twitter and other social media related businesses all deploy (but not exclusively) Cluster Analysis

IV. Classification Analysis

Classification Analysis is a systematic process to obtain important and relevant information about data, and metadata – data about data. The classification analysis helps identifying to which of a set of categories different types of data belong. Classification analysis is closely linked to cluster analysis as the classification can be used to cluster data.

Example Of Classification Analysis:  Google Mail (Gmail) Google uses algorithms that are capable of classifying your email as legitimate or mark it as spam. This is done based on data that is linked with the email or the information that is in the email, for example certain words or attachments that indicate spam.

V. Regression analysis

Regression analysis tries to define the dependency between variables. It assumes a one-way causal effect from one variable to the response of another variable. Independent variables can be affected by each other but it does not mean that this dependency is both ways as is the case with correlation analysis. A regression analysis can show that one variable is dependent on another but not vice-versa.Regression analysis is used to determine different levels of customer satisfactions and how they affect customer loyalty and how service levels can be affected by for example the weather.

Example of Regression Analysis The website eHarmony uses a regression model that matches two individual singles based on 29 variables to find the best partner. Data Mining can potentially make you find the love of your life or predict your divorce..

www.insidedevices.orgData mining enables businesses, organizations, governments and scientists to find and select the most important and relevant information. This information can be used to create models that can help make predictions how people or systems will behave so you can anticipate on it.

The more data you have the better the models will become that you can create using the data mining techniques, resulting in more business value for your organisation.

sources:

  1. www.venturebeat.com
  2. The New York Review Of Books
  3. The Piper Report
  4. Mashable
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Seven Conflicts of Systemic Interoperability On The Development Of The Internet of Things


In previous blogs both the benefits and drawbacks of systemic interoperability of the internet of things were discussed. The outcomes are conflicting since there are:

Seven Conflicts in Systemic Interoperability On The Development Of The Internet of Things

  1. Systemic interoperability increases the magnitude of all of the potential drawbacks mentioned.
  2. Interoperating at a systemic level increases the potential for noisy communications.
  3. www.insidedevices.orgA lack of systemic interoperability protects against security risks by creating systemic gaps which viruses cannot cross.It protects against privacy risks by keeping data separated, so that compromising one set of data does not compromise another.
  4. Joining systems increases the vulnerability of each system and the data that each system keeps.
  5. Systemic interoperability increases the potential for perverse outcomes by increasing the degree of interference in the system.
  6. Systemic interoperability increases problems related to lock-in because any upgrades to one system need to keep the interoperation of the two systems intact, making upgrading more difficult.
  7. Systemic interoperability significantly increases all of the potential drawbacks of interoperability on IoTs seems to suggest that IoT development should be oriented towards more closed systems.

This may be outweighed by the benefits of joining systems, and it is likely that the optimal solution will vary among different uses.