International Journal of Data Mining, Modelling and Management 

About: International Journal of Data Mining, Modelling and Management is an academic journal published by Inderscience Publishers. The journal publishes majorly in the area(s): Computer science & Cluster analysis. It has an ISSN identifier of 1759-1171. Over the lifetime, 237 publications have been published receiving 1288 citations. The journal is also known as: IJDMMM.

Is an ordinal class structure useful in classifier learning

Abstract: In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, the order information of the problem, essentially relying on the assumption that the ordinal structure of the set of class labels is also reflected in the topology of the instance space. The purpose of this paper is to investigate, on an experimental basis, the validity of this assumption. Moreover, we seek to answer the question to what extent existing techniques and learning algorithms for ordinal classification are able to exploit order information and which properties of these techniques are important in this regard.

Privacy preserving record linkage approaches

Abstract: Privacy-preserving record linkage is a very important task, mostly because of the very sensitive nature of the personal data. The main focus in this task is to find a way to match records from among different organisation data sets or databases without revealing competitive or personal information to non-owners. Towards accomplishing this task, several methods and protocols have been proposed. In this work, we propose a certain methodology for preserving the privacy of various record linkage approaches and we implement, examine and compare four pairs of privacy preserving record linkage methods and protocols. Two of these protocols use n-gram based similarity comparison techniques, the third protocol uses the well known edit distance and the fourth one implements the Jaro-Winkler distance metric. All of the protocols used are enhanced by private key cryptography and hash encoding. This paper presents also a blocking scheme as an extension to the privacy preserving record linkage methodology. Our comparison is backed up by extended experimental evaluation that demonstrates the performance achieved by each of the proposed protocols.

Mining event histories: a social science perspective

Abstract: We explore how recent data mining-based tools developed in domains such as biomedicine or text mining for extracting interesting knowledge from sequence data could be applied to personal life course data. We focus on two types of approaches: 'survival' trees that attempt to partition the data into homogeneous groups regarding their survival characteristics, i.e., the duration until a given event occurs and the mining of typical discriminating episodes. We show how these approaches may fruitfully complement the outcome of more classical event history analyses and single out some specific issues raised by their application to socio-demographic data.

Using trajectories for collaborative filtering-based POI recommendation

Abstract: Current mobile guides often suffer from the following problems: a long knowledge acquisition process of recommending relevant points of interest (POIs), the lack of social navigation support, and the challenge of making implicit user-generated content (e.g., trajectories) useful. Collaborative filtering (CF) is a promising solution for these problems. This article employs CF to mine GPS trajectories for providing Amazon-like POI recommendations. Three CF methods are designed: simple_CF, freq_CF (considering visit frequencies of POIs), and freq_seq_CF (considering both user’s preferences and spatio-temporal behaviour). With these, services like “after visiting …, people similar to you often went to …” can be provided. The methods are evaluated with two GPS datasets. The results show that the CF methods can provide more accurate predictions than simple location-based methods. Also considering visit frequencies (popularity) of POIs and spatio-temporal motion behaviour (mainly the ways in which POIs are visited) in CF can improve the predictive performance.

A relational perspective on spatial data mining

Abstract: Remote sensing and mobile devices nowadays collect a huge amount of spatial data, which have to be analysed in order to discover interesting information about economic, social and scientific problems. However, the presence of a spatial dimension adds some problems to data mining tasks. The geometrical representation and relative positioning of spatial objects implicitly define spatial relationships, whose efficient computation requires a tight integration of the data mining system with the spatial DBMS. The interaction between spatially close objects causes different forms of autocorrelation, whose effect should be considered to improve the predictive accuracy of induced models and patterns. Units of analysis are typically composed of several spatial objects with different properties and their structure cannot be easily accommodated by classical double entry tabular data. In the paper, a way is shown to face these problems when a (multi-)relational data mining approach is considered for spatial data analy...