The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The growing complexity and variability characterizing markets have induced scholars and marketers to propose new segmentation approaches. Recent research has shown that including the context in which a transaction occurs in customer behavior models, improves the ability of predicting their behavior. However, no systematic research has studied whether contextual information really matters in market...
A variety of services have recently been provided depending on highly developed networks and personal equipment. With these advances, connecting this equipment has become increasingly more complicated. Problems such as an increase in no-connection and determining the cause have become difficult in some cases because software is often updated to keep up with advancements in services or security. Telecom...
This paper presents a new keyword extraction algorithm for Chinese news Web pages using lexical chains and word co-occurrence combined with frequency features, cohesion features, and corelation features. A lexical chain is an external performance consistency by semantically related words of a text, and is the representation of the semantic content of a portion of the text. Word co-occurrence distribution...
For multi-view learning, existing methods usually exploit originally provided features for classifier training, which ignore the latent correlation between different views. In this paper, semantic features integrating information from multiple views are extracted for pattern representation. Canonical correlation analysis is used to learn the representation of semantic spaces where semantic features...
An association rule (AR) is a common knowledge model in data mining that describes an implicative co-occurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an "antecedent rArr consequent" rule. A variant of the AR is the weighted association rule (WAR). With regard to a marketing context, this paper introduces a...
Distance computation is one of the most computationally intensive operations employed by many data mining algorithms. Performing such matrix computations within a DBMS creates many optimization challenges. We propose techniques to efficiently compute Euclidean distance using SQL queries and user-defined functions (UDFs). We concentrate on efficient Euclidean distance computation for the well-known...
For many data mining applications, it is necessary to develop algorithms that use unlabeled data to improve the accuracy of the supervised learning. Co-Training is a popular semi-supervised learning algorithm. It assumes that each example is represented by two or more redundantly sufficient sets of features (views) and these views are independent given the class. However, these assumptions are not...
With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window...
Weka4WS is an extension of the Weka toolkit to support remote execution of data mining tasks as grid services. A first version of Weka4WS supporting concurrent execution of multiple data mining tasks on remote grid nodes has been presented in a previous work. In this paper we present a new version supporting also the composition and execution of data mining workflows on a grid. This new version of...
Behavior is increasingly recognized as a key component in business intelligence and problem-solving. Different from traditional behavior analysis, which mainly focus on implicit behavior and explicit business appearance as a result of business usage and customer demographics, this paper proposes the field of Behavior Informatics and Analytics (BIA), to support explicit behavior involvement through...
In this paper we consider the problem of discovering frequent temporal patterns in a database of temporal sequences, where a temporal sequence is a set of items with associated dates and durations. Since the quantitative temporal information appears to be fundamental in many contexts, it is taken into account in the mining processes and returned as part of the extracted knowledge. To this end, we...
This paper describes a multi-dimensional knowledge discovery and data mining (KDD) methodology that aims at discovering actionable knowledge related to Internet threats, taking into account domain expert guidance and the integration of domain-specific intelligence during the data mining process. The objectives are twofold: i) to develop global indicators for assessing the prevalence of certain malicious...
We introduce s-kNN, a nearest neighbor based spatial data mining algorithm. It belongs to the class of vector-geometry based algorithms that reason on complex spatial objects instead of point measurements. In contrast to most methods in this class, it does on the fly spatial computations that cannot be replaced by a pre-processing step without sacrificing efficiency. The key is a partial evaluation...
Longitudinal data consist of the repeated measurements of some variables which describe the dynamics of a domain(process or phenomenon) over time. They can be analyzed in order to explain what event may cause the transition from a state into the next one during the evolution of the domain. Generally, approaches to this explanation problem rely on the exclusive usage of domain knowledge, while an analysis...
The real-world process of generating a large spatio-temporal data collection presents a very difficult technical problem. First, this process is very expensive, requiring a lot of various high-technology software tools and modern hardware infrastructure (sensors, servers, GPS infrastructure etc.) installations; second, the recorded trajectories sometimes cannot represent any special traffic or movement...
In some applications, the whole structure of the target data can be represented naturally in "multi-structured graphs" that are complex graphs whose vertices consist of aset of structured data such as itemsets, sequences and so on. To catch the strong affinity relationship in multi-structured graphs, in this paper, we propose an algorithm named HFMG to discover novel and meaningful frequent...
Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Previous research on action rule discovery usually required the extraction of classification rules before constructing any action rule. This paper gives anew approach for generating association-type action rules. The notion of frequent action sets and Apriori-like strategy generating...
Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on the use of a distance measure defined either on numerical attributes or on categorical attributes. However, in fields such as road traffic and medicine, datasets are composed of numerical and categorical attributes. Recently, there have been several proposals...
Word meaning disambiguation has always been an important problem in many computer science tasks, such as information retrieval and extraction. One of the problems,faced in automatic word sense discovery, is the number of different senses a word can have. Often, senses are dominated by some other, more frequent ones. Discovering such dominated meanings can significantly improve quality of many text-related...
INGENS is a prototype of GIS which integrates a geographic knowledge discovery engine to mine several kinds of spatial KDD objects from the topographic maps stored in a spatial database. In this paper we describe the main principles of an inductive spatial database in INGENS. Inductive database allows to keep permanent KDD objects and integrate database technology with systems for the geographic knowledge...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.