Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

https://www.hse.ru/data/2010/11/01/1223500185/InformationSciences.pdf

Mining gene expression data with pattern structures in formal concept analysis

Graph pattern matching is typically defined in terms of subgraph isomorphism, which makes it an np-complete problem. Moreover, it requires bijective functions, which are often too restrictive to characterize patterns in emerging applications. We propose a class of graph patterns, in which an edge denotes the connectivity in a data graph within a predefined number of hops. In addition, we define matching based on a notion of bounded simulation, an extension of graph simulation. We show that with this revision, graph pattern matching can be performed in cubic-time, by providing such an algorithm. We also develop algorithms for incrementally finding matches when data graphs are updated, with performance guarantees for dag patterns. We experimentally verify that these algorithms scale well, and that the revised notion of graph pattern matching allows us to identify communities commonly found in real-world networks.

Graph Pattern Matching.

This is the first part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this first part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 on developing FCA-based methods for knowledge processing. We also give an overview of the literature on FCA extensions such as pattern structures, logical concept analysis, relational concept analysis, power context families, fuzzy FCA, rough FCA, temporal and triadic concept analysis and discuss scalability issues.

https://publications.hse.ru/mirror/pubs/share/folder/hgte0pjz18/direct/114480023.pdf

Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques

Developing algorithms that discover all frequently occurring subgraphs in a large graph database is computationally extensive, as graph and subgraph isomorphisms play a key role throughout the computations. Since subgraph isomorphism testing is a hard problem, fragment miners are exponential in runtime. To alleviate the complexity issue, we propose to introduce a bias in the projection operator and instead of using the costly subgraph isomorphism projection, one can use a polynomial projection having a semantically valid structural interpretation. In this paper, our purpose is to present LC-mine, a generic and efficient framework to mine frequent subgraphs by the means of local consistency techniques used in the constraint programming field. Two instances of the framework based on the arc consistency technique are developed and presented in this paper. The first instance follows a breadth-first order, while the second is a pattern-growth approach that follows a depth-first search space exploration strategy. Then, we prove experimentally that we can achieve an important performance gain without or with nonsignificant loss of discovered patterns in terms of quality.

/pdf/lc-mine-a-framework-for-frequent-subgraph-mining-with-local-2mgrifehtr.pdf

LC-mine: a framework for frequent subgraph mining with local consistency techniques

The projection problem (conceptual graph projection, homomorphism, injective morphism, i¾?-subsumption, OI-subsumption) is crucial to the efficiency of relational learning systems. How to manage this complexity has motivated numerous studies on learning biases, restricting the size and/or the number of hypotheses explored. The approach suggested in this paper advocates a projection operator based on the classical arc consistency algorithm used in constraint satisfaction problems. This projection method has the required properties : polynomiality, local validation, parallelization, structural interpretation. Using the arc consistency projection, we found a generalization operator between labeled graphs. Such an operator gives the structure of the classification space which is a concept lattice.

/pdf/arc-consistency-projection-a-new-generalization-relation-for-1kcs0txppx.pdf

Arc Consistency Projection: A New Generalization Relation for Graphs

This chapter presents a formal model to learning from examples represented by labelled graphs This formal model is based upon lattice theory and in particular Galois lattices We widen the domain of formal concept analysis, by the use of the Galois lattices model with structural descriptions of examples and concepts The operational implementation of our model, called "Graal" (for GRAph And Learning) constructs a Galois lattice for any description language provided that the operations of comparison and generalization are determined for that language These operations exist in the case of labelled graphs satisfying an partial order relation (homomorphism) This paper is concerned as well with the known problems regarding propositionalization (ie the transformation of a structural description in a propositional description) Using classical lattice results, we have a formal model for the transformation of a structural machine learning problem into a propositional one

/pdf/some-links-between-formal-concept-analysis-and-graph-mining-4jl40owi3o.pdf

Some Links between Formal Concept Analysis and Graph Mining

In Model Driven Engineering (MDE), a Model Transforma-tion is a specialized program, often composed of a set of rules to transform models. The Model Transformation By Example (MTBE) approach aims to assist the developer by learning model transformations from source and target model examples.In a previous work, we proposed an approach which takes as input a fragmented source model and a target model, and produces a set of fragment pairs that presents the many-to-many match-ing links between the two models. In this paper, we propose to mine model transformation patterns (that can be later transformed in trans-formation rules) from the obtained matching links. We encode our models into labeled graphs that are then classified using the GRAAL approach to get meaningful common subgraphs. New transformation patterns are then found from the classification of the matching links based on their graph ends. We evaluate the feasibility of our approach on two represen-tative small transformation examples.

https://hal-auf.archives-ouvertes.fr/hal-01075523/document

Learning Model Transformation Patterns using Graph Generalization

Concept or Galois Lattices (CL) provide a productive framework for a variety of problems that arise in Knowledge Discovery in Databases (KDD). This paper introduces this special issue of Applied Artificial Intelligence devoted to applications of Concept Lattices for KDD (CLKDD). The papers in this volume come from a call for papers issued after the first International Workshop on Concept Lattice-based Theory, Methods and Tools for KDD held in July 2001 at Stanford University. Another special issue devoted to algorithms and methods of CLKDD will appear in the Journal of Experimental and Theoretical Artificial Intelligence.

Michel Liquière

Papers

LC-mine: a framework for frequent subgraph mining with local consistency techniques

Arc Consistency Projection: A New Generalization Relation for Graphs

Some Links between Formal Concept Analysis and Graph Mining

Learning Model Transformation Patterns using Graph Generalization

Introduction concept lattice-based theory, methods and tools for knowledge discovery in databases: Applications