SeqTrie: An index for data mining applications (original) (raw)

2001

Most of the established companies have accumulated masses of data from their customers for decades. With the e-commerce applications growing rapidly, the companies will have a significant amount of data in months not in years. Data Mining, also known as Knowledge Discovery in Databases (KDD), is to find trends, patterns, correlations, anomalies in these databases which can help us to make accurate future decisions. Mining Association Rules is one of the main application areas of Data Mining. Given a set of customer transactions on items, the aim is to find correlations between the sales of items. We consider Association Mining in large database of customer transactions. We give an overview of the problem and explain approaches that have been used to attack this problem. We then give the description of the Apriori Algorithm and show results that are taken from Gima Türk A.Ş. a large Turkish supermarket chain. We also use two statistical methods: Principal Component Analysis and k-means to detect correlations between sets of items.

A Survey on Data Mining Algorithm for Market Basket Analysis

Abstracts -Association rule mining identifies the remarkable association or relationship between a large set of data items. With huge quantity of data constantly being obtained and stored in databases, several industries are becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, lossleader analysis, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales and conserve inventory by focusing on the point of sale transaction data. This work acts as a broad area for the researchers to develop a better data mining algorithm. This paper presents a survey about the existing data mining algorithm for market basket analysis.

MBA: Market Basket Analysis Using Frequent Pattern Mining Techniques

International Journal on Recent and Innovation Trends in Computing and Communication, 2023

This Market Basket Analysis (MBA) is a data mining technique that uses frequent pattern mining algorithms to discover patterns of co-occurrence among items that are frequently purchased together. It is commonly used in retail and e-commerce businesses to generate association rules that describe the relationships between different items, and to make recommendations to customers based on their previous purchases. MBA is a powerful tool for identifying patterns of co-occurrence and generating insights that can improve sales and marketing strategies. Although a numerous works has been carried out to handle the computational cost for discovering the frequent itemsets, but it still needs more exploration and developments. In this paper, we introduce an efficient Bitwise-Based data structure technique for mining frequent pattern in large-scale databases. The algorithm scans the original database once, using the Bitwise-Based data representations as well as vertical database layout, compared to the well-known Apriori and FP-Growth algorithm. Bitwise-Based technique enhance the problems of multiple passes over the original database, hence, minimizes the execution time. Extensive experiments have been carried out to validate our technique, which outperform Apriori, Éclat, FP-growth, and H-mine in terms of execution time for Market Basket Analysis.

A Study on Market Basket Analysis Using a Data Mining Algorithm

2013

Association rule mining is the power ful tool now a days in Data mining. It identifies the correlation between the items in large databases. A typical example of Association rule mining is Market Basket analysis. In this method or approach it examines the buying habits of the customers by identifying the associations among the items purchased by the customers in their baskets. This helps to increase in the sales of a particular product by identifying the frequent items purchased by the customers. This paper mainly focuses on the study of the existing data mining algorithm for Market Basket data.

Market Basket Analysis: A Data Mining Tool for Maximizing Sales & Customer Support

international journal of research in computer application & management, 2012

Data mining is becoming increasingly common in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing commonly use data mining to reduce costs, enhance research and increase sales. Market Basket Analysis (MBA,-Association Analysis) is a mathematical modeling technique based upon the theory that if you buy a certain group of items, you are likely to buy another group of items. It is used to analyze the customer's purchasing behavior and helps in increasing the sales and maintain inventory by focusing on the point of sale transaction data. Market Basket Analysis is the discovery of relations or correlations among a set of items which are actually transactions made by customer's purchases. MBA also known as affinity analysis has emerged as the next step in the evolution of the retail merchandising and promotion. MBA allows leading retailers to quickly & easily look at the size, contents, & value of their customer's market basket to understand how products are purchased together It helps the retailers to drill down into customer buying patterns over time to precisely target & understand specific, combination of products departments, brands,categories, & even time of day. Association rule which is the output of the MBA helps to specify the combination of the products; those should be sold in combination. The aim of the analysis is to determine the strength of all the association rules among a set of items. The strength of the association is measured by the support and confidence of the rule.

International Journal of Emerging Technology and Advanced Engineering A Study on Market Basket Analysis Using a Data Mining Algorithm

Data Mining in Databases: Languages and Indices

Studies in Big Data

Database systems methodologies and technology can provide a significant support to data mining processes. In this chapter we explore approaches which address the integration between data mining activities and DBMSs from different perspectives. More specifically, we focus on (i) specialized query languages which allow to define complex data mining tasks through the submission of query requests, and (ii) indices, i.e., physical data structures designed to improve the performance of mining algorithms.

Indexing and Data Access Methods for Database Mining

2002

Most of today's techniques for data mining and association rule mining (ARM) in particular, are really "flat file mining", since the database is typically dumped to an intermediate flat file that is input to the mining software. Previous research in integrating ARM with databases mainly looked at exploiting language (SQL) as a tool for implementing mining algorithms. In this paper we explore an alternative approach, using various data access methods and systems programming techniques to study the efficiency of mining data.

Data Mining and Data Warehousing

Today in organizations, the developments in the transaction processing technology requires that, amount and rate of data capture should match the speed of processing of the data into information which can be utilized for decision making. A data warehouse is a subject- oriented, integrated, time-variant and non-volatile collection of data that is required for decision making process. Data mining involves the use of various data analysis tools to discover new facts, valid patterns and relationships in large data sets. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. OLTP is customer-oriented and is used for transaction and query processing by clerks, clients and information technology professionals. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives and analysts. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications.

SeqTrie: An index for data mining applications (original) (raw)

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