The aim of the conference is to bring together researchers from all over the world who deal with machine learning and data mining in order to discuss the recent status of the research and to direct further developments. Basic research papers as well as application papers are welcome.All kinds of applications are welcome but special preference will be given to multimedia related applications, biomedical applications, and webmining. MLDM´2013 is the 9th event in a series of MLDM events that have been originally started out as a workshop. Paper submissions should be related but not limited to any of the following topics:association rulesAudio Miningcase-based reasoning and learningclassification and interpretation of images, text, videoconceptional learning and clusteringGoodness measures and evaluaion (e.g. false discovery rates)inductive learning including decision tree and rule induction learningknowledge extraction from text, video, signals and imagesmining gene data bases and biological data basesmining images, temporal-spatial data, images from remote sensingmining structural representations such as log files, text documents and HTML documentsmining text documentsorganisational learning and evolutional learningprobabilistic information retrievalSelection biasSampling methodsSelection with small samplessimilarity measures and learning of similaritystatistical learning and neural net based learningvideo miningvisualization and data miningApplications of ClusteringAspects of Data MiningApplications in MedicineAutoamtic Semantic Annotation of Media ContentBayesian Models and MethodsCase-Based Reasoning and Associative MemoryContent-Based Image RetrievalDecision TreesDeviation and Novelty DetectionFeature Grouping, Discretization, Selection and TransformationFeature LearningFrequent Pattern MiningHigh-Content Analysis of Microscopic Images in Medicine, Biotechnology and ChemistryLearning and adaptive controlLearning/adaption of recognition and perceptionLearning for Handwriting RecognitionLearning in Image Pre-Processing and SegmentationLearning in process automationLearning of internal representations and modelsLearning of appropriate behaviourLearning of OntologiesLearning of Semantic Inferencing RulesLearning of Visual OntologiesLearning robotsMining Financial or Stockmarket DataMining Images in Computer VisionMining Images and TextureMining Motion from SequenceNeural MethodsNetwork Analysis and Intrusion DetectionNonlinear Function Learning and Neural Net Based LearningReal-Time Event Learning and DetectionSpeech AnalysisStatistical and Conceptual Clustering Methods: BasicsStatistical and Evolutionary LearningSubspace MethodsSupport Vector MachinesSymbolic Learning and Neural Networks in Document ProcessingText MiningTime Series and Sequential Pattern MiningMining Social Media