Multi-source Relations for Contextual Data Mining in Learning Analytics

International audienceTrack Academic research: comprehensive evaluations of recent innovations in learning and student analytics approaches. Context and Purpose The goals of Learning Analytics (LA) are manifold, among which helping students to understand their academic progress and improving their learning process, which are at the core of our work. To reach this goal, LA relies on educational data: students' traces of activities on VLE, or academic, socio-demographic information, information about teachers, pedagogical resources, curricula, etc. The data sources that contain such information are multiple and diverse. Data mining, specifically pattern mining, aims at extracting valuable and understandable information from large datasets [2]. In our work, we assume that multiple educational data sources form a rich dataset that can result in valuable patterns. Mining such data is thus a promising way to reach the goal of helping students. However, heterogeneity and interdependency within data lead to a high computational complexity. We thus aim at designing low complex pattern mining algorithms that mine multi-source data, taking into consideration the dependency and heterogeneity among sources. The patterns formed are meaningful and interpretable, they can thus be directly used for students

Concept analysis-based association mining from linked data: A case in industrial decision making

International audienceLinked data (LD) is a rich format increasingly exploited in knowledge discovery from data (KDD). To that end, LD is typically structured as graph, but can also fit the multi-relational data mining (MRDM) paradigm, e.g. as multiple types and object properties may be used in the dataset. Formal concept analysis (FCA) has been successfully used as theoretical framework for KDD in a variety of applications , primely in clustering and association rule mining (ARM) tasks. As FCA applicability to LD is limited by its single data table input format, relational concept analysis (RCA) was introduced as a MRDM extension that successfully deals with links in the data, including cyclic ones. While RCA has been mainly adapted for conceptual clustering in the past, we present here an RCA-based ARM method. It exploits the iterative nature of pattern generation to cut cyclic references with a minimal loss of information. The utility of the rules discovered by our method has been validated by an application as a decision support in the aluminum die casting industry

a general framework to encode heterogeneous information sources for contextual pattern mining

Traditional pattern mining methods usually work on single data sources. However, in practice, there are often multiple and heterogeneous information sources. They collectively provide contextual information not available in any single source alone describing the same set of objects, and are useful for discovering hidden contextual patterns. One important challenge is to provide a general methodology to mine contextual patterns easily and efficiently. In this paper, we propose a general framework to encode contextual information from multiple sources into a coherent representation - -Contextual Information Graph (CIG). The complexity of the encoding scheme is linear in both time and space. More importantly, CIG can be handled by any single-source pattern mining algorithms that accept taxonomies without any modification. We demonstrate by three applications of the contextual association rule, sequence and graph mining, that contextual patterns providing rich and insightful knowledge can be easily discovered by the proposed framework. It enables Contextual Pattern Mining (CPM) by reusing single-source methods, and is easy to deploy and use in real-world systems. © 2012 ACM.Special Interest Group on Information Retrieval (ACM SIGIR); ACM SIGWEBTraditional pattern mining methods usually work on single data sources. However, in practice, there are often multiple and heterogeneous information sources. They collectively provide contextual information not available in any single source alone describing the same set of objects, and are useful for discovering hidden contextual patterns. One important challenge is to provide a general methodology to mine contextual patterns easily and efficiently. In this paper, we propose a general framework to encode contextual information from multiple sources into a coherent representation - -Contextual Information Graph (CIG). The complexity of the encoding scheme is linear in both time and space. More importantly, CIG can be handled by any single-source pattern mining algorithms that accept taxonomies without any modification. We demonstrate by three applications of the contextual association rule, sequence and graph mining, that contextual patterns providing rich and insightful knowledge can be easily discovered by the proposed framework. It enables Contextual Pattern Mining (CPM) by reusing single-source methods, and is easy to deploy and use in real-world systems. © 2012 ACM