Association algorithm in Data mining

Association algorithm in Data mining

Sequence clustering is an algorithm that gathers the similar paths. Or collect sequences of data that contains the related events. That collected similar data actually shows a sequence of events or transitions between states in a dataset. Such as a series of web clicks between two specific times can be seen by this.

With the help of algorithm we can examine all probabilities of transitions and can measure the differences in the data set. In any data set the distances between all the possible sequences can also be examined or calculated by the algorithm. We can determine that which sequence can be the best as input for clustering. For your better understanding, a sequence clustering algorithm may help in finding the best path to store “similar” nature items in a warehouse.

Association algorithm in Data mining

For your better understanding I am giving a following example:

Association Rule: Let I = {i1, i2, …, im} be a set of basic items.

Suppose there are many transitional ‘T’ in a specific time.

Let D be a set of transactions, where each transaction T is a set of items such that T ⊆I. And then TID indicates a unique transaction identifier.

An association rule is an implication of the form X.

Association algorithm in Data mining

Data Mining Algorithms (Analysis Services – Data Mining)

The data mining algorithm is the mechanism that creates a data mining model. To create a model, an algorithm first analyzes a set of data and looks for specific patterns and trends. The algorithm uses the results of this analysis to define the parameters of the mining model. These parameters are then applied across the entire data set to extract actionable patterns and detailed statistics.

The mining model that an algorithm creates can take various forms, including:

• A set of rules that describe how products are grouped together in a transaction.
• A decision tree that predicts whether a particular customer will buy a product.
• A mathematical model that forecasts sales.
• A set of clusters that describe how the cases in a dataset are related.

Microsoft SQL Server Analysis Services provides several algorithms for use in your data mining solutions. These algorithms are a subset of all the algorithms that can be used for data mining. You can also use third-party algorithms that comply with the OLE DB for Data Mining specification. For more information about third-party algorithms, see Plugin Algorithms.

 Types of Data Mining Algorithms

Analysis Services includes the following algorithm types:

• Classification algorithms predict one or more discrete variable, based on the other attributes in the dataset. An example of a classification algorithm is the Microsoft Decision Trees Algorithm.
• Regression algorithms predict one or more continuous variables, such as profit or loss, based on other attributes in the dataset. An example of a regression algorithm is the Microsoft Time Series Algorithm.
• Segmentation algorithms divide data into groups, or clusters, of items that have similar properties. An example of a segmentation algorithm is the Microsoft Clustering Algorithm.
• Association algorithms find 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. An example of an association algorithm is the Microsoft Association Algorithm.
• Sequence analysis algorithms summarize frequent sequences or episodes in data, such as a Web path flow. An example of a sequence analysis algorithm is the Microsoft Sequence Clustering Algorithm.

 Applying the Algorithms

Choosing the best algorithm to use for a specific business task can be a challenge. While you can use different algorithms to perform the same business task, each algorithm produces a different result, and some algorithms can produce more than one type of result. 

For example, you can use the Microsoft Decision Trees algorithm not only for prediction, but also as a way to reduce the number of columns in a dataset, because the decision tree can identify columns that do not affect the final mining model.

You also do not have to use algorithms independently. In a single data mining solution you can use some algorithms to explore data, and then use other algorithms to predict a specific outcome based on that data. 

For example, you can use a clustering algorithm, which recognizes patterns, to break data into groups that are more or less homogeneous, and then use the results to create a better decision tree model. You can use multiple algorithms within one solution to perform separate tasks, for example by using a regression tree algorithm to obtain financial forecasting information, and a rule-based algorithm to perform a market basket analysis.

Mining models can predict values, produce summaries of data, and find hidden correlations. To help you select algorithms for your data mining solution, the following table provides suggestions for which algorithms to use for specific tasks.

The Microsoft Sequence Clustering algorithm is a sequence analysis algorithm provided by Microsoft SQL Server Analysis Services. You can use this algorithm to explore data that contain events that can be linked by following paths, or sequences. The algorithm finds the most common sequences by grouping, or clustering, sequences that are identical. The following are some examples of sequences:

Data that describes the click paths that are created when users navigate or browse a Web site.

Data that describes the order in which a customer adds items to a shopping cart at an online retailer.

This algorithm is similar in many ways to the Microsoft Clustering algorithm. However, instead of finding clusters of cases that contain similar attributes, the Microsoft Sequence Clustering algorithm finds clusters of cases that contain similar paths in a sequence.

Example

The Adventure Works Cycles Web site collects information about what pages site users visit, and about the order in which the pages are visited. Because the company provides online ordering, customers must log in to the site. This provides the company with click information for each customer profile.

By using the Microsoft Sequence Clustering algorithm on this data, the company can find groups, or clusters, of customers who have similar patterns or sequences of clicks. The company can then use these clusters to analyze how users move through the Web site, to identify which pages are most closely related to the sale of a particular product, and to predict which pages are most likely to be visited next.

Association algorithm in Data mining

By using the query data mining is used to examine or explore the data. These queries can be found in the data warehouse. Explore the data in data mining helps in reporting, planning strategies, finding meaningful patterns etc.

Large amount of data can be transformed into a meaningful form with the help of data mining. That data can be numbing, facts or any real time information like sales figures, cost, meta data etc.

This Information may be the patterns and the relationships amongst the data that can provide helpful & useful information.