Image mining is concerned with knowledge discovery in image databases. We present a data mining approach to find association rules based on image content. The Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and Association rule mining. The purpose of our experiments is to explore the feasibility of data mining approach.. Result...

Association rule mining is an important mining function because of its completeness. It has been even used to generate classi ers and to segment database. However the current model of association rule mining poses problems regarding exibility while handling evolving databases. The expensive process of massive rule discovery discourages frequent application of mining algorithm for exploratory mi...

The process of discovering interesting and unexpected rules from large data sets is known as association rule mining. The typical approach is to make strong simplifying assumptions about the form of the rules, and limit the measure of rule quality to simple properties such as support or confidence. Support and confidence limit the level of interestingness of the generated rules. Comprehensibili...

In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using particle swarm optimization algorithm (PSO). PSO algorithm is population based heuristic search technique used for s...

Data Mining is used to discover the knowledge from large amount of databases and transform it into a flexible structure. Association rule mining (ARM) is the essential part of data mining process. Finding good quality of association rules between items in large databases has been an important and challenging association rule mining problem. The rules mined through ARM algorithms are used for de...

Association rule mining is a powerful model of data mining used for finding hidden patterns in large databases. One of the great challenges of data mining is to protect the confidentiality of sensitive patterns when releasing database to third parties. Association rule hiding algorithms sanitize database such that certain sensitive association rules cannot be discovered through association rule...

Spatial data refer to any data about objects that occupy real physical space. Attributes within spatial databases usually include spatial information. Spatial data refers to the numerical or categorical values of a function at different spatial locations. Spatial metadata refers to the descriptions of the spatial configuration. Application of classical association rule mining concepts to spatia...

Traditional association rule mining consider support confident measures to find out frequent item sets, it assumes all items are having equal significance. Where as weighted association rule mining assigns weights to items based on different aspects. Because researchers are more concerned with qualitative aspects of attributes (e.g. significance), as compared to considering only quantitative on...

Association rule mining is the most popular technique in the area of data mining. The main task of this technique is to find the frequent patterns by using minimum support thresholds decided by the user. The Apriori algorithm is a classical algorithm among association rule mining techniques. This algorithm is inefficient because it scans the database many times. Second, if the database is large...

In two party privacy preserving association rule mining, the issue to securely compare two integers is considered as the bottle neck to achieve maximum privacy. Recently proposed fully homomorphic encryption (FHE) scheme by Dijk et.al. can be applied in secure computation. Kaosar, Paulet and Yi have applied it in preserving privacy in two-party association rule mining, but its performance is no...