JDM: The Java API for Data Mining October 29, 2007
Posted by nhabibi in Data Mining, Java.2 comments
Data mining tools were traditionally provided in products with vendor-specific interfaces. JDM defines a common Java API to interact with data mining systems. In this way, data Mining clients can be coded against a single API that is independent of the underlying data mining system. It is developed by Java Community Data Mining Expert Group.
JDM includes supervised and unsupervised learning algorithms such as decision trees, neural networks, Naive Bayes, Support Vector Machine, K-Means, and Apriori.
In fact, JDM provides for data mining systems what JDBC did for relational databases.
For using JDM, you must follow a few steps:
1-Identify the data whit a URL
2-Create a loical representaion of your data
3-Specify the type of model (clustering,etc.)
4-Specify the parameters to your data mining algorithm
5-Create a build task
6-Execute the task
7-Get result
You must get the JDM API source codes and code your program against it.
There are also Data mining Web services that provide an opportunity to facilitate integration of multiple data mining software implementations in a single application, enable a service oriented architecture.
More Information:
-Java Specification Request 247: Java Data Mining (JDM) 2.0
-F. Sommers, “Mine Your Own Data with the JDM API”
-M. F. Hornick, E. Macade, S. Vankayala, “Java Data mining Concepts”
-Robert Chu,” Web Services Standards for Data Mining”
Fuzzy & Data Minig September 20, 2007
Posted by nhabibi in Artificial Intelligence, Data Mining, Fuzzy, Soft Computing.1 comment so far
Fuzzy sets are sets whose elements have degrees of membership. Fuzzy sets have been introduced by Professor Lotfali Askarzadeh (1965) as an extension of the classical notion of set.
In classical set theory, an element either belongs or does not belong to the set. By contrast, fuzzy set theory an element belongs to the set, with membership degree in interval [0-1].
An example:
Assume that we have four students with bellow scores in biology exam:
Richard, 20
frank, 18
Anna, 14
Juli, 11
We want to define “A = set of Good Student in Biology Class“:
1- in classical set theory: A = {Richard, Frank}
2- in fuzzy set theory: A = { (Richard,1) , (Mark,0.7) , (Anna,0.3) , (Juli,0.1) }
In fact, we have associated a membership degree to each member, according to goodness of their score.
Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic.
Fuzzy concept has application in many areas, specially in control systems.
In many topics, after proposing an algorithm, the fuzzy version of the algorithm is proposed and evaluated against the original one.
Here, is our report, in Persian, that introduces data mining in general, Apriori algorithm to extract association rules, a fuzzy-based Apriori algorithm, Decision Tree and fuzzy-based Decision Tree.
More information:
- Zadeh’s 1965 paper on Fuzzy Sets (It has more that 5000 citations till now!)
