Advantages of Information Granulation in Clustering Algorithms

Kużelewska, Urszula

doi:10.1007/978-3-642-29966-7_9

Cited by 13 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-to implement in Apache Mahout environment other clustering methods, such as DBSCAN or EM, and to perform experiments taking advantage of their ability to evaluate a number of clusters automatically, -to apply a granulation and clustering method described in (Kużelewska, 2013), which allow to adjust resolution as well as accuracy of generated results, -to experiment on other data, like binary (particularly on shopping baskets) to test efficiency of the proposed approach, -to reduce items dimensionality by applying data mining attribute selection methods as well as techniques from text processing.…”

Section: Resultsmentioning

confidence: 99%

“…Clustering is a domain of data mining which had been applied in a wide range of problems, among others, in pattern recognition, image processing, statistical data analysis and knowledge discovery (Kużelewska, 2013). The aim of cluster analysis is organising a collection of patterns (usually represented as a vector of measurements, or a point in a multi-dimensional space) into clusters based on their similarity (Jain, Murty, & Flynn, 1999).…”

Section: Clustering Methods In Recommender Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Clustering Algorithms in Hybrid Recommender System on MovieLens Data

Kużelewska

2014

Studies in Logic, Grammar and Rhetoric

View full text Add to dashboard Cite

Decisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting web sites to explore, choosing needed information in search engine results or deciding which product to buy in an on-line store. Recommender systems are electronic applications, the aim of which is to support humans in this decision making process. They are widely used in many applications: adaptive WWW servers, e-learning, music and video preferences, internet stores etc. In on-line solutions, such as e-shops or libraries, the aim of recommendations is to show customers the products which they are probably interested in. As input data the following are taken: shopping basket archives, ratings of the products or servers log files.The article presents a solution of recommender system which helps users to select an interesting product. The system analyses data from other customers' ratings of the products. It uses clustering methods to find similarities among the users and proposed techniques to identify users' profiles. The system was implemented in Apache Mahout environment and tested on a movie database. Selected similarity measures are based on: Euclidean distance, cosine as well as correlation coefficient and loglikehood function.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Clustering Methods In Recommender Systemsmentioning

confidence: 99%

Clustering Algorithms in Hybrid Recommender System on MovieLens Data

Kużelewska

2014

Studies in Logic, Grammar and Rhetoric

View full text Add to dashboard Cite

show abstract

“…Clustering: Clustering techniques have been applied in different domains such as, pattern recognition, image processing, statistical data analysis and knowledge discovery [51]. Clustering algorithm tries to partition a set of data into a set of sub-clusters in order to discover meaningful groups that exist within them [52].…”

Section: Model-based Techniquesmentioning

confidence: 99%

Recommendation systems: Principles, methods and evaluation

Isinkaye

Folajimi

Ojokoh

2015

Egyptian Informatics Journal

1,005

437

View full text Add to dashboard Cite

On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.Ó 2015 Production and hosting by Elsevier B.V. on behalf

show abstract

“…Clustering is a domain of data mining which had been applied in a wide range of problems, among others, in pattern recognition, image processing, statistical data analysis and knowledge discovery [7]. The aim of cluster analysis is organising a collection of patterns (usually represented as a vector of measurements, or a point in a multi-dimensional space) into clusters based on their similarity [5].…”

Section: Description Of the Clustering Algorithm Used In The Systemmentioning

confidence: 99%

A Modified Clustering Algorithm DBSCAN Used in a Collaborative Filtering Recommender System for Music Recommendation

Kużelewska

Wichowski

2015

Advances in Intelligent Systems and Computing

Self Cite

View full text Add to dashboard Cite

Abstract. Searching in huge amount of information available on the internet is undoubtedly a challenging task. A lot of new web sites are created every day, containing not only text, but other types of resources: e.g. songs, movies or images. As a consequence, a simple search result list from search engines becomes insufficient. Recommender systems are the solution supporting users in finding items, which are interesting for them. These items may be information as well as products, in general. The main distinctive feature of recommender systems is taking into account personal needs and taste of users. Collaborative filtering approach is based on users' interactions with the electronic system. Its main challenge is generating on-line recommendations in reasonable time coping with large size of data. Appropriate tool to support recommender systems in increasing time efficiency are clustering algorithms, which find similarities in off-line mode. Commonly, it involves decreasing of prediction accuracy of final recommendations. This article presents an approach based on clustered data, which prevents the negative consequences, keeping high time efficiency. An input data are clusters of similar items and searching the items for recommendation is limited to the elements from one cluster.

show abstract

Advantages of Information Granulation in Clustering Algorithms

Cited by 13 publications

References 9 publications

Clustering Algorithms in Hybrid Recommender System on MovieLens Data

Clustering Algorithms in Hybrid Recommender System on MovieLens Data

Recommendation systems: Principles, methods and evaluation

A Modified Clustering Algorithm DBSCAN Used in a Collaborative Filtering Recommender System for Music Recommendation

Contact Info

Product

Resources

About