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With the steady development of information technology, efficient, fast and safe enterprise financial management has received the focused attention of enterprise managers. Not only that, the integration of enterprise departments and financial operations is getting closer and closer. With the increasing business volume of enterprises, the interactive data generated by the data management system of the financial departments of enterprises has become increasingly prominent. Based on the research and analysis of enterprise financial data, combined with Apriori, clustering, decision tree and other data mining technologies, this paper extracts and utilizes massive financial data generated by the enterprise financial department in the daily management process, and designs and implements an intelligent analysis system of enterprise financial data based on data mining. It provides helpful financial information for the financial management department and senior management and assists the enterprise in making quick decisions.

Association rule mining (ARM) is the data-mining process for finding all association rules in datasets matching user-defined measures of interest such as support and confidence. Usually, ARM proceeds by mining all frequent itemsets — a step known to be very computationally intensive — from which rules are then derived in a straight forward manner. In general, mining all frequent itemsets prunes the space by using the downward closure (or anti-monotonicity) property of support which states that no itemset can be frequent unless all of its subsets are frequent. A large number of papers have addressed the problem of ARM but not many of them have focused on scalability over very large datasets (i.e. when datasets contain a very large number of transactions). In this paper, we propose a new model for representing data and mining frequent itemsets that is based on the P-tree technology for compression and faster logical operations over vertically structured data and on set enumeration trees for fast itemset enumeration. Experimental results presented hereinafter show big improvements for our approach over large datasets when compared to other contemporary approaches in the literature.

When Apriori was first introduced as an algorithm for discovering association rules in a database of market basket data, the problem of generating the candidate set of the large set was a bottleneck in Apriori's performance, both in space and computational requirements. At first, many unsuccessful attempts were made to improve the generation of a candidate set. Later, other algorithms that out performed Apriori were developed that generate association rules without using a candidate set. They used the counting property of association rules instead of generating the candidate set as Apriori does. However, the Apriori concept has been used in many different areas other than counting market basket items, and the candidate generation problem remains a bottleneck issue. The approach described here improves the overall time and space requirements by eliminating the need for a hash table/tree of formation for the candidate set.

Colon cancer is one of the most common spread cancers in the world, which leads to total death of 10%. Prediction of onset of cancer, and the cause of its development in these patients can be of an enormous help and relief to those affected, as they can get back their “normal” life. Data mining and machine learning are important intelligent tools for classification, prediction and hidden relation extraction between patient information. We collected data from Shahid Faghihi Hospital in Shiraz. Features collected are as follows: Gender, age, duration of cancer before surgery, number of times the patients used bathroom, taking anti-inflammatory drug prednisolone, duration of drug use and dosage, kind of surgery and number of times consulted and retreatment of surgery, incontinence, etc. After pre-processing and data cleaning stages, effective features were extracted, and also occurrence of cancer predicts by using different classification algorithms. Then association rule mining algorithms like Apriori were used for obtaining any internal hidden relation between entries. Approaching them with different algorithms and assessing them with support vector machine was with highest prediction accuracy (84%). Due to unbalanced dataset, we chose cost sensitive support vector machine. In another aspect, after applying Apriori algorithm, the conditions of non-inflammation were extracted based on dataset features. Some significant outcomes are in what follows. If surgery treatment or diagnosed was less than 5 years, the possibility of developing colon cancer is lower. Also, as the duration of disease increases, the possibility of reoperation increases, as confirmed by the interiors. Since this issue with these features was raised for the first time in this paper at the suggestion of internists, early detection of cancer and also the extraction of effective laws can be of help to the medical community. In future, to get higher accuracy, the improvement of the dataset in terms of number of samples and colonoscopy image features is considered.

This chapter introduces the concept of association rules, a form of local-pattern discovery in an unsupervised learning system. Association rules are used to uncover relationships between inherently unrelated data items. The terminology, notation, and the processes used with association rules are discussed, and a brief overview of three basic rule inference algorithms is given.