Wyniki wyszukiwania - BazTech

1

Customer’s Purchase Prediction Using Customer Segmentation Approach for Clustering of Categorical Data

Singh Juhi, Mittal Mandeep

Management and Production Engineering Review

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2021

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Vol. 12, No. 2

57--64

EN

Traditional clustering algorithms which use distance between a pair of data points to calculate their similarity are not suitable for clustering of boolean and categorical attributes. In this paper, a modified clustering algorithm for categorical attributes is used for segmentation of customers. Each segment is then mined using frequent pattern mining algorithm in order to infer rules that helps in predicting customer’s next purchase. Generally, purchases of items are related to each other, for example, grocery items are frequently purchased together while electronic items are purchased together. Therefore, if the knowledge of purchase dependencies is available, then those items can be grouped together and attractive offers can be made for the customers which, in turn, increase overall profit of the organization. This work focuses on grouping of such items. Various experiments on real time database are implemented to evaluate the performance of proposed approach.

2

Evaluation of Apriori Algorithm on Retail Market Transactional Database to get Frequent Itemsets

Gaikwad Pooja R., Shailesh D. Kamble Shailesh D., Thakur Nileshsingh V., Patharkar Akshay S.

Annals of Computer Science and Information Systems

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2017

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Vol. 10

187--192

EN

In Data mining the concept of association rule mining (ARM) is used to identify the frequent itemsets from large datasets. It defines frequent pattern mining from larger datasets using Apriori algorithm \& FP-growth algorithm. The Apriori algorithm is a classic traditional algorithm for the mining all frequent itemsets and association rules. But, the traditional Apriori algorithm have some limitations i.e. there are more candidate sets generation \& huge memory consumption, etc. Still, there is a scope for improvement to modify the existing Apriori algorithm for identifying frequent itemsets with a focus on reducing the computational time and memory space. This paper presents the analysis of existing Apriori algorithms and results of the traditional Apriori algorithm. Experimentation carried out on transactional database i.e. retail market for getting frequent itemsets. The traditional Apriori algorithm is evaluated in terms of support and confidence of transactional itemsets.

3

Negative feature selection algorithm for anomaly detection in real time

Hryniów K., Dzieliński A.

Studia Informatica : systems and information technology

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2011

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Vol. 1-2(15)

15--23

EN

Anomaly detection methods are of common use in many fields, including databases and large computer systems. This article presents new algorithm based on negative feature selection, which can be used to find anomalies in real time. Proposed algorithm, called Negative Feature Selection algorithm (NegFS) can be also used as first step for preprocessing data analyzed by neural networks, rule-based systems or other anomaly detection tools, to speed up the process for large and very large datasets of different types.

4

Zastosowanie technik eksploatacji danych do budowania grup studenckich

Zakrzewska D.

Studia Informatica

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2010

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Vol. 31, nr 2B

335-346

PL

Znalezienie grup studentów o podobnych preferencjach umożliwi dopasowanie do ich potrzeb systemu nauczania na odległość. Celem pracy jest porównanie różnych technik eksploracji danych do budowania grup. Rozważa się zastosowanie klasyfikacji bez nadzoru oraz po nadzorem, jak również wykrywania wzorców sekwencji.

EN

Finding student groups of similar preferences enables to adjust e-learning systems according to their needs. In the paper, it is compared usage of different data mining techniques for creating learners' groups. It is considered application of supervised and unsupervised classification as well as frequent pattern mining.

5

Building student models in adaptive E-learning systems

Zakrzewska D.

Przegląd Elektrotechniczny

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2010

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R. 86, nr 8

151-154

EN

Adaptivity of an e-learning system is one of the most important feature deciding on its performance. Finding student models enables to adjust e-learning systems according to their needs. In the paper, it is proposed to use frequent pattern mining to find learner characteristics and acc to build student groups of similar needs accordingly. The proposed method is compared with the solution , where frequent patterns are found on clusters. Some experimental results for real and artificially generated data are presented.

PL

Adaptacyjność jest ważną cechą decydującą o skuteczności systemów edukacyjnych. Znalezienie modeli studentów, umożliwia dopasowanie systemów do ich potrzeb. W pracy, zaproponowano użycie częstych wzorców do znalezienia charakterystyk studentów i zbudowania grup o podobnych potrzebach. Zaproponowana metoda jest porównana z rozwiązaniem, w którym wzorce są budowane na klastrach.