A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages ...

In field of data mining, mining the frequent itemsets from huge amount of data stored in database is an important task. Frequent itemsets leads to formation of association rules. Various methods have been proposed and implemented to improve the efficiency of Apriori algorithm. This paper focuses on comparing the improvements proposed in classical Apriori Algorithm for frequent item set mining. ...

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 artificial bee colony algorithm (ABC). The Artificial bee colony algorithm is an optimization algorithm based on the...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. This paper presents privacy preserving association rule mining across vertically partitioned data. We present an efficient algorithm to discover association rules with minimum levels of support and confidence, from heterogeneous data distributed across 2 parties, while preventing eit...

Data Mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining association rules from databases with crisp values. However, the data in many real-world applications consist of interval and fuzzy values. In th...

Clustering is a core problem in data-mining with innumerable applications spanning many fields. A key difficulty of effective clustering is that for unlabelled data a ‘good’ solution is a somewhat ill-defined concept, and hence a plethora of valid measures of cluster quality have been devised. Most clustering algorithms optimize just one such objective (often implicitly) and are thus limited in...

In data mining, the association rules are used to find for the associations between the different items of the transactions database. As the data collected and stored, rules of value can be found through association rules, which can be applied to help managers execute marketing strategies and establish sound market frameworks. This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth) to...