Mining frequent itemsets from the large transactional database is a very critical and important task. Many algorithms have been proposed from past many years, But FP-tree like algorithms are considered as very effective algorithms for efficiently mine frequent item sets. These algorithms considered as efficient because of their compact structure and also for less generation of candidates itemse...

The existing studies are mostly focused on the multiple graphs scenario. With some modifications, the mining methodology can be extended to the single graph scenario [30]. Washio and Motoda [56] conducted a survey on graph-based data mining. Holder et al. [21] proposed SUBDUE to do subgraph pattern discovery based on minimum description length and background knowledge. The most popular graph pa...

Concern about national security has increased after the 26/11 Mumbai attack. In this paper we look at the use of missing value and clustering algorithm for a data mining approach to help predict the crimes patterns and fast up the process of solving crime. We will concentrate on MV algorithm and Apriori algorithm with some enhancements to aid in the process of filling the missing value and iden...

The paper presents a hypothesis language declaration formalism and a corresponding refinement operator that successfully combines the levelwise search principle with a first-order hypothesis language and thus provides an improvement to the so-called optimal refinement operators that are commonly used in descriptive ILP. The refinement operator is based on the candidate generation procedure of t...

This paper presents the Shape based Movement Pattern (ShaMP) algorithm, an algorithm for extracting Movement Patterns (MPs) from network data that can later be used (say) for prediction purposes. The principal advantage offered by the ShaMP algorithm is that it lends itself to parallelisation so that very large networks can be processed. The concept of MPs is fully defined together with the rea...

Association Rule Mining is an area of data mining that focuses on pruning candidate keys. An Apriori algorithm is the most commonly used Association Rule Mining. This algorithm somehow has limitation and thus, giving the opportunity to do this research. This paper introduces a new way in which the Apriori algorithm can be improved. The modified algorithm introduces factors such as set size and ...

In this paper we propose DCP, a new algorithm for solving the Frequent Set Counting problem, which enhances Apriori. Our goal was to optimize the initial iterations of Apriori, i.e. the most time consuming ones when datasets characterized by short or medium length frequent patterns are considered. The main improvements regard the use of an innovative method for storing candidate set of items an...

The organization, management and accessing of information in better manner in various data warehouse applications have been active areas of research for many researchers for more than last two decades. The work presented in this paper is motivated from their work and inspired to reduce complexity involved in data mining from data warehouse. A new algorithm named VS_Apriori is introduced as the ...

In genetic studies, complex diseases are often analyzed searching for marker patterns that play a significant role in the susceptibility to the disease. In this paper we consider a dataset regarding periodontitis, that includes the analysis of nine genetic markers for 148 individuals. We analyze these data by using a novel subgroup discovering algorithm, named APRIORI-B, that is based on APRIOR...

Apriori and Eclat are the best-known basic algorithms for mining frequent item sets in a set of transactions. In this paper I describe implementations of these two algorithms that use several optimizations to achieve maximum performance, w.r.t. both execution time and memory usage. The Apriori implementation is based on a prefix tree representation of the needed counters and uses a doubly recur...