Algorithms

There are various data mining algorithms which can be used to build the mining model. But choosing the right algorithm for the right business task is critical. Different algorithms can be used to do the same business tasks but each algorithm produces different results.
The various types of algorithms are as follows:
1. Classification algorithm predicts one or more discrete variables, based on the other attributes in the dataset. eg: Microsoft Decision Trees Algorithm.

2. Regression algorithm predicts one or more continuous variables, such as profit or loss, based on other attributes in the dataset. eg: Microsoft Time Series Algorithm.

3. Segmentation algorithm divides data into groups, or clusters, of items that have similar properties. eg: Microsoft Clustering Algorithm.

4. Association algorithm finds correlations between different attributes in a dataset. The most common application of this kind of algorithm is for creating association rules, which can be used in a market basket analysis. eg: Microsoft Association Algorithm.

5. Sequence analysis algorithm summarizes frequent sequences or episodes in data, such as a Web path flow. eg: Microsoft Sequence Clustering Algorithm.

A data mining application can adopt different algorithms for different functions, for example we can use segmentation algorithms for exploring data and regression algorithms for prediction functionalities.

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Data Mining -2

Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g., rule-based systems) and opaque in others such as neural networks. Moreover, some data-mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery.

Metadata, or data about a given data set, are often expressed in a condensed data-minable format, or one that facilitates the practice of data mining. Common examples include executive summaries and scientific abstracts.

Data mining relies on the use of real world data. This data is extremely vulnerable to collinearity precisely because data from the real world may have unknown interrelations. An unavoidable weakness of data mining is that the critical data that may expose any relationship might have never been observed. Alternative approaches using an experiment-based approach such as Choice Modelling for human-generated data may be used. Inherent correlations are either controlled for or removed altogether through the construction of an experimental design.

Recently, there were some efforts to define a standard for data mining, for example the CRISP-DM standard for analysis processes or the Java Data-Mining Standard. Independent of these standardization efforts, freely available open-source software systems like RapidMiner and Weka have become an informal standard for defining data-mining processes.

Data Mining -1

We are talking data mining in our last post and we were talking background of data mining. We continue our talk on data mining in this post.


Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, non-statistician users have the opportunity to identify key attributes of business processes and target opportunities. However, abdicating control of this process from the statistician to the machine may result in false-positives or no useful results at all.


Although data mining is a relatively new term, the technology is not. For many years, businesses have used powerful computers to sift through volumes of data such as supermarket scanner data to produce market research reports (although reporting is not always considered to be data mining). Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of data analysis.


The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user.