Association rule mining is a very important research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining. Traditional association rule algorithms adopt an iterative method to discovery, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm called ABBM is proposed ...

Mining of frequent itemsets is a fundamental data mining task. Past research has proposed many efficient algorithms for the purpose. Recent work also highlighted the importance of using constraints to focus the mining process to mine only those relevant itemsets. In practice, data mining is often an interactive and iterative process. The user typically changes constraints and runs the mining al...

The typical data mining process is characterized by the prospective and iterative application of a variety of different data mining algorithms from an algorithm toolbox. While it would be desirable to check many different algorithms and algorithm combinations for their performance on a database, it is often not feasible because of time and other resource constraints. This paper investigates the...

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...

Mining sequential patterns is a key objective in the field of data mining due to its wide range of applications. Given a database of sequences, the challenge is to identify patterns which appear frequently in different sequences. Well known algorithms have proved to be efficient, however these algorithms do not perform well when mining databases that have long frequent sequences. We present CAM...

identifying clusters or clustering is an important aspect of data analysis. it is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. it is a main task of exploratory data mining, and a common technique for statistical data analysis this paper proposed an improved version of k-means algorithm, namely persistent k...

Big data is a broad term for datasets so large and complex that the traditional data processing applications are inadequate, so i2mapreduce based framework for incremental and iterative computations are done in big data. State level processing computation easily retrieve the data and also time consuming. Incremental and iterative mapreducemapreduce is the most widely used big data processing to...

the concept of intelligently controlling the search process of gravitational search algorithm (gsa) is introduced to develop a novel data mining technique. the proposed method is called fuzzy gsa miner (fgsa-miner). at first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major ro...