Data Mining and Knowledge Discovery

Items from 1 to 14 out of 14 results

article

A Mathematical Morphology Based Scale Space Method for the Mining of Linear Features in Geographic Data

Min Wang, Yee Leung, Chenhu Zhou, Tao Pei, more

Data Mining and Knowledge Discovery > 2006 > 12 > 1 > 97-118

This paper presents a spatial data mining method MCAMMO and its extension L_MCAMMO designed for discovering linear and near linear features in spatial databases. L_MCAMMO can be divided into two basic steps: first, the most suitable re-segmenting scale is found by MCAMMO, which is a scale space method with mathematical morphology operators; second, the segmented result at this scale is re-segmented...

article

Structural Periodic Measures for Time-Series Data

Michail Vlachos, Philip S. Yu, Vittorio Castelli, Christopher Meek

Data Mining and Knowledge Discovery > 2006 > 12 > 1 > 1-28

This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity detection and we introduce new periodic distance measures for time-series sequences. We combine these new measures with an effective metric tree index structure...

article

An Erratum on #x201C;Pushing Convertible Constraints in Frequent Itemset Mining#x201D;

Jian Pei, Jiawei Han, Laks V. S. Lakshmanan

Data Mining and Knowledge Discovery > 2006 > 12 > 1 > 119-119

The online version of the original article can be found at http://dx.doi.org/10.1023/B:DAMI.0000023674.74932.4c

article

Data Clustering with Partial Supervision

Abdelhamid Bouchachia, Witold Pedrycz

Data Mining and Knowledge Discovery > 2006 > 12 > 1 > 47-78

Clustering with partial supervision finds its application in situations where data is neither entirely nor accurately labeled. This paper discusses a semi-supervised clustering algorithm based on a modified version of the fuzzy C-Means (FCM) algorithm. The objective function of the proposed algorithm consists of two components. The first concerns traditional unsupervised clustering while the second...

article

Computing LTS Regression for Large Data Sets

Peter J. Rousseeuw, Katrien Driessen

Data Mining and Knowledge Discovery > 2006 > 12 > 1 > 29-45

Data mining aims to extract previously unknown patterns or substructures from large databases. In statistics, this is what methods of robust estimation and outlier detection were constructed for, see e.g. Rousseeuw and Leroy (1987). Here we will focus on least trimmed squares (LTS) regression, which is based on the subset of h cases (out of n) whose least squares fit possesses the smallest sum of...

article

Structural Hidden Markov Models Using a Relation of Equivalence: Application to Automotive Designs

D. Bouchaffra, J. Tan

Data Mining and Knowledge Discovery > 2006 > 12 > 1 > 79-96

Standard hidden Markov models (HMM's) have been studied extensively in the last two decades. It is well known that these models assume state conditional independence of the observations. Therefore, they are inadequate for classification of complex and highly structured patterns. Nowadays, the need for new statistical models that are capable to cope with structural time series data is increasing. We...

article

Organizing Multiple Data Sources for Developing Intelligent e-Business Portals

Jia Hu, Ning Zhong

Data Mining and Knowledge Discovery > 2006 > 12 > 2-3 > 127-150

Enterprise applications usually involve huge, complex, and persistent data to work on, together with business rules and processes. In order to represent, integrate, and use the information coming from the huge, distributed, multiple sources, we present a conceptual model with dynamic multi-level workflows corresponding to a mining-grid centric multi-layer grid architecture, for multi-aspect analysis...

article

Mining Adaptive Ratio Rules from Distributed Data Sources

Jun Yan, Ning Liu, Qiang Yang, Benyu Zhang, more

Data Mining and Knowledge Discovery > 2006 > 12 > 2-3 > 249-273

Different from traditional association-rule mining, a new paradigm called Ratio Rule (RR) was proposed recently. Ratio rules are aimed at capturing the quantitative association knowledge, We extend this framework to mining ratio rules from distributed and dynamic data sources. This is a novel and challenging problem. The traditional techniques used for ratio rule mining is an eigen-system analysis...

article

Mining Multiple Data Sources: Local Pattern Analysis

Shichao Zhang, Mohammed J. Zaki

Data Mining and Knowledge Discovery > 2006 > 12 > 2-3 > 121-125

Shichao Zhang: is a senior research fellow in the Faculty of Information Technology at UTS, Australia, and a chair professor of Automatic Control at BUAA, China. He received his PhD degree in computer science from Deakin University, Australia. His research interests include data analysis and smart pattern discovery. He has published about 35 international journal papers, including 6 in IEEE/ACM Transactions,...

article

Discovering Classification from Data of Multiple Sources

Charles X. Ling, Qiang Yang

Data Mining and Knowledge Discovery > 2006 > 12 > 2-3 > 181-201

In many large e-commerce organizations, multiple data sources are often used to describe the same customers, thus it is important to consolidate data of multiple sources for intelligent business decision making. In this paper, we propose a novel method that predicts the classification of data from multiple sources without class labels in each source. We test our method on artificial and real-world...

article

Fast Distributed Outlier Detection in Mixed-Attribute Data Sets

Matthew Eric Otey, Amol Ghoting, Srinivasan Parthasarathy

Data Mining and Knowledge Discovery > 2006 > 12 > 2-3 > 203-228

Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinical diagnosis, from detecting anomalous defects in materials to fraud and intrusion detection. Over the past decade, researchers in data mining and statistics have addressed the problem of outlier detection using both parametric...

article

Bridging Local and Global Data Cleansing: Identifying Class Noise in Large, Distributed Data Datasets

Xingquan Zhu, Xindong Wu, Qijun Chen

Data Mining and Knowledge Discovery > 2006 > 12 > 2-3 > 275-308

To cleanse mislabeled examples from a training dataset for efficient and effective induction, most existing approaches adopt a major set oriented scheme: the training dataset is separated into two parts (a major set and a minor set). The classifiers learned from the major set are used to identify noise in the minor set. The obvious drawbacks of such a scheme are twofold: (1) when the underlying data...

article

Reinforcing Web-object Categorization Through Interrelationships

GUI-RONG Xue, Yong Yu, Dou Shen, Qiang Yang, more

Data Mining and Knowledge Discovery > 2006 > 12 > 2-3 > 229-248

Existing categorization algorithms deal with homogeneous Web objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspect of the inter-object relationship is not sufficient to fully reveal the true categories of Web objects. In this paper, we propose a novel categorization algorithm,...

article

Sequential Pattern Mining in Multi-Databases via Multiple Alignment

Hye-Chung Kum, Joong Hyuk Chang, Wei Wang

Data Mining and Knowledge Discovery > 2006 > 12 > 2-3 > 151-180

To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple...

Filter options

Volume:
12
Year:
2006

Publication date

Set your own date range

Number

2-3 (8)
1 (6)

Keywords

CLUSTERING (2)
OUTLIER DETECTION (2)
ANOMALY DETECTION (1)
APPROXIMATE SEQUENTIAL PATTERN (1)
AUTOCORRELATION (1)
BREAKDOWN VALUE (1)
CATEGORIZATION (1)
CLASS DISCRIMINATION (1)
CLASS NOISE (1)
CLASSIFICATION (1)
CLICKTHROUGH DATA (1)
DATA CLEANSING (1)
DATA MINING ALGORITHM (1)
DATA STREAM MINING (1)
DATA STREAMS (1)
DISTRIBUTED DATA MINING (1)
DYNAMIC MULTI-LEVEL WORKFLOWS (1)
EIGEN SYSTEM ANALYSIS (1)
GLOBAL SEQUENTIAL PATTERN (1)
HIDDEN MARKOV MODELS (1)
INTELLIGENT E-BUSINESS PORTALS (1)
INTERRELATED WEB OBJECTS (1)
ITERATIVE REINFORCEMENT (1)
LEARNING CLASSIFICATIONS FROM UNLABELED DATA OF MULTIPLE SOURCES (1)
LEARNING FROM MULTIPLE SOURCES OF DATA (1)
LINEAR BELT (1)
LINEAR MODEL (1)
LINEAR REGRESSION. (1)
LOCAL STRUCTURES (1)
MACHINE LEARNING (1)
MATHEMATICAL MORPHOLOGY (1)
METRIC INDEX (1)
MINING DYNAMIC DATA (1)
MINING LOCAL PATTERN (1)
MIXEDATTRIBUTE DATA SETS (1)
MULTI-DATABASE MINING (1)
MULTI-LAYER GRID (1)
MULTIPLE ALIGNMENT (1)
MULTIPLE SOURCE DATA MINING (1)
NEW SOLUTIONS FOR MULTIPLE DATA SOURCE MINING (1)
PARTIAL SUPERVISION (1)
PERIODICITY ESTIMATION (1)
PERIODOGRAM (1)
PHASE-INVARIANT MATCHING (1)
RATIO RULE (1)
REGRESSION (1)
RELATION OF EQUIVALENCE (1)
ROBUST ESTIMATION (1)
ROBUST STATISTICS (1)
SCALE SPACE THEORY (1)
SEISMIC BELT (1)
SEQUENTIAL PATTERNS (1)
SPATIAL DATA MINING (1)
STATISTICAL DECODING (1)
STRUCTURAL DECODING (1)
THE WISDOM WEB (1)
more

INFONA - science communication portal

Data Mining and Knowledge Discovery

Add recipient

Sending message cancelled

Are you sure you want to cancel sending this message?

Send message

Filter options

Publication date

Date range setting

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.

Number

Keywords

Reporting an error / abuse

Sending the report failed

Accessibility options