Informatics Publishing Limited

K. Veningston ¹, Deepa Kanmani ¹, Sreeja S. Pillai ¹

Affiliations
1 Department of Computer Science and Engineering at Karunya University, Coimbatore, IN

Abstract

Mining frequent item-sets or patterns from an online transactional database is one of the fundamental and essential operations in many data mining applications. Apriori and FP-growth are some of the examples for the existing frequent pattern mining algorithms. Only the frequent items sets and their counts are found out by these algorithms. They do not consider anything about the time stamps associated with the transaction. Each transaction database usually consists of time stamp of each transaction. This time stamp is a sequence of characters denoting the date and time at which a certain event occurred. This paper extends the existing frequent pattern mining algorithms to take into account time stamp of each transaction. And also discovers a new type of patterns whose frequency dramatically changes over time which is defined as transitional patterns. The frequency of the transitional patterns may increase or decrease at some time points in a transaction database. These patterns capture the dynamic behavior of frequent patterns in a transaction database. This paper also studies a new concept called significant milestones, which are time points at which the frequency of the pattern changes most significantly. This paper objective is to find out such transitional patterns and their significant milestones that considering the timestamp of each transaction, a modified transitional pattern mining algorithm is presented.

Keywords

Frequent Patterns, Data Mining, Transitional Patterns, Transaction Database.

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K. Veningston ¹, Deepa Kanmani ¹, T. M. Nitha ¹

Affiliations
1 Department of Computer Science and Engineering at Karunya University, Coimbatore, IN

Abstract

Using form based interface, user can easily extract data from database without the knowledge of the formal query language and the underlying database schema. Form should be simple and easy to understand and it should support as many queries as possible. In this paper a framework is studied for generating the forms automatically according to the user needs. This automated technique does not require interface developers or end users to manually build forms. This paper also provides a mechanism for extending the forms to support future similar queries for which the clustering approach identified. It is to adjust forms to reflect the most current querying needs without creating new query form.

Keywords

Query Form, Database Schema, Query Language, Clustering, Query Evaluation.

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T. Neethu Mohan ¹, Deepa Kanmani ², G. Hemalatha ²

Affiliations
1 Computer Science and Engineering in Karunya University, Coimbatore, IN
2 Computer Science and Engineering Department in Karunya University, Coimbatore, IN

Abstract

The existing top k- retrieval algorithms help the users for searching and retrieving the needed products from the databases. But there is a different view for the problem i.e. how the sellers can identify the user preferred features for a product. The problem is to identify the best attributes so that the product is highly visible to the customers. In this paper, several solutions for the problem are considered. It includes exact and approximation algorithms. The exact algorithm is a maximal frequent item set mining algorithm. This algorithm uses random walk in Dualize and Advance algorithm as its foundation. The approximation algorithms are based on greedy heuristics. Two greedy heuristics are described for solving the problem. This greedy heuristics is a modification of the existing greedy algorithm for the attribute selection. Even though the problem considered is novel, this paper surveys on the above specified algorithms.

Keywords

Data Mining, Knowledge and Data Engineering Tools and Techniques, Marketing, Mining Methods and Algorithms, Retrieval Models.

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Sreeja S. Pillai ¹, K. Veningston ¹, Deepa Kanmani ¹

Affiliations
1 Department of Computer Science and Engineering at Karunya University, Coimbatore, IN

Abstract

The process of extracting interesting implicit knowledge from large information repositories like relational databases, data warehouse etc. and summarizing into useful information is called as Data Mining. Data Mining is also known as knowledge discovery in databases, knowledge extraction, business intelligence etc. Data mining should be applicable to any kind of data repository as well as to transient data such as data streams. A transactional database consists of a file where each record represents a transaction. Frequent patterns are patterns that occur frequently in data. There are various approaches proposed for frequent pattern mining. But this paper is discussing about transitional patterns that are patterns whose frequency dramatically changes over time and various approaches of frequent pattern mining that do not consider the time stamp of each transaction.

Keywords

Frequent Patterns, Data Mining, Transitional Patterns, Transaction Database.

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